{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Biogeoscience case study\n",
    "\n",
    "The two case studies in this folder from climate science and biogeosciences follow the QAD-questionnaire and method-selection flow chart in the following Review paper (included in the tigramite github tutorial folder):\n",
    "\n",
    "Runge, J., Gerhardus, A., Varando, G., Eyring, V. & Camps-Valls, G. Causal inference for time series. Nat. Rev. Earth Environ. 10, 2553 (2023).\n",
    "\n",
    "A list of methods with software to address selected QAD problems appears at the end of that Review paper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example will demonstrate the use of causal inference based techniques to investigate the causal effect of air temperature (Tair) on ecosystem respiration (Reco) from data that also includes gross primary production (GPP) and shortwave radiation (Rg). To better illustrate non-parametric causal effect estimation, this case study considers a synthetic system with known quantitative ground truth:\n",
    "\n",
    "\\begin{align}\\label{eq:biogeoscience_model}\n",
    "\\begin{split}\n",
    "\\text{Rg}_t &= \\lvert 280~\\sin(t \\pi /365)^2 + 50~\\lvert\\sin(t \\pi /365)\\rvert\\eta^{\\text{Rg}}_t\\rvert \\\\\n",
    "\\text{Tair}_t &= 0.8~\\text{Tair}_{t-1} + 0.02~\\text{Rg}_t + 5 \\eta^{\\text{Tair}}_t \\\\\n",
    "\\text{GPP}_t &= \\lvert 0.2~\\text{GPP}_{t-1} + 0.002~\\text{Tair}_t~\\text{Rg}_t + 3 \\eta^{\\text{GPP}}_t \\rvert \\\\ \n",
    "\\text{Reco}_t &= \\lvert 0.3~\\text{Reco}_{t-1} + 0.9~\\text{GPP}_t~0.8^{0.12(\\text{Tair}_t-15)} + 2 \\eta^{\\text{Reco}}_t \\rvert\n",
    "\\end{split}\n",
    "\\end{align}\n",
    "\n",
    "In these equations, which are interpreted as an SCM, the $\\eta^{\\cdot}_t$ are mutually independent standard normal noise terms, except for Tair where $\\eta^{\\text{Tair}}_t=\\eta_t+\\frac{1}{4}\\epsilon_t^3$ (standard normal noises $\\eta$ and $\\epsilon$) with a cubic exponent to represent more extreme temperatures. The SCM exhibits a unimodal relationship between Reco and Tair (see the interventional ground truth in the figures below), which has also been found in real data (see paper).\n",
    "\n",
    "The analysis will first illustrate causal discovery and then causal effect estimation. Let's start with some imports of standard python packages as well as the tigramite causal inference package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.transforms as mtransforms\n",
    "\n",
    "import sys\n",
    "from copy import deepcopy\n",
    "\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.stats import gaussian_kde\n",
    "import warnings\n",
    "from sklearn.exceptions import DataConversionWarning\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "import tigramite\n",
    "import tigramite.data_processing as pp\n",
    "import tigramite.plotting as tp\n",
    "\n",
    "from tigramite.models import LinearMediation, Models\n",
    "from tigramite.causal_effects import CausalEffects\n",
    "\n",
    "from tigramite.pcmci import PCMCI\n",
    "from tigramite.independence_tests.robust_parcorr import RobustParCorr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data generation and plotting\n",
    "\n",
    "The steps closely follow the QAD-template (Tab. 1 and Fig. 2 flow chart in Review paper). As opposed to the climate example, here all variables (nodes) are already defined as daily continuously-valued time series. The next question is about creating a stationary dataset (Fig. 2 flow chart). Unlike in the climate example, here a setting with multiple datasets (multiple sites) is considered. In the considered synthetic example, stationarity is fulfilled by construction (apart from seasonality shared by all sites), as the sites are just different realizations of the same SCM. Hence, the time series from the different sites can be simply aggregated (pooled). To alleviate the seasonal non-stationarity, only the period April-September (model months) is considered (see figure below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time series length is 6 years\n",
    "T = 365*6 + 1\n",
    "\n",
    "# 4 Variables\n",
    "N = 4\n",
    "\n",
    "# We model 5 measurement sites\n",
    "M = 5\n",
    "\n",
    "data_dict = {}\n",
    "mask_dict = {}\n",
    "for site in range(M):\n",
    "\n",
    "    modeldata_mask = np.ones((T, N), dtype='int')\n",
    "    for t in range(T):\n",
    "        # April to September\n",
    "        if 90 <= t % 365 <= 273:\n",
    "            modeldata_mask[t,:] = 0\n",
    "            \n",
    "    mask_dict[site] = modeldata_mask\n",
    "\n",
    "    modeldata = np.zeros((T,N))\n",
    "    random_state = np.random.RandomState(site)\n",
    "    noise = random_state.randn(T, N)\n",
    "    noise[:, 1] += 0.25*random_state.randn(T)**3\n",
    "        \n",
    "    for t in range(1, T):\n",
    "        modeldata[t,0] = np.abs(280.*np.abs(np.sin((t)*np.pi/365.))**2 + 50.*np.abs(np.sin(t*np.pi/365.))*noise[t,0])\n",
    "        modeldata[t,1] = 0.8*modeldata[t-1,1] + 0.02*modeldata[t,0] + 5*noise[t,1]  \n",
    "        modeldata[t,2] = np.abs(0.2* modeldata[t-1, 2] + 0.002*modeldata[t,0] * modeldata[t,1] + 3*noise[t,2]) \n",
    "        modeldata[t,3] = np.abs(0.3*modeldata[t-1,3] + 0.9*modeldata[t,2] * 0.8**(0.12*(modeldata[t,1]-15)) + 2*noise[t,3])\n",
    "    data_dict[site] = modeldata\n",
    "\n",
    "# Variable names\n",
    "var_names = ['Rg', 'Tair', 'GPP', 'Reco']\n",
    "\n",
    "# Init Tigramite dataframe object\n",
    "dataframe = pp.DataFrame(data=data_dict, \n",
    "                    mask = mask_dict,\n",
    "                    analysis_mode = 'multiple',\n",
    "                    var_names=var_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_axes = tp.plot_timeseries(dataframe,\n",
    "                   grey_masked_samples='data',\n",
    "                   adjust_plot=False,\n",
    "                   color = 'red',\n",
    "                   alpha=0.6, \n",
    "                   data_linewidth=0.3,\n",
    "                   selected_dataset=0)\n",
    "\n",
    "for index in range(1, len(data_dict)):\n",
    "    adjust_plot = False\n",
    "    if index == M - 1: \n",
    "        adjust_plot = True\n",
    "    color = ['red', 'green', 'blue', 'yellow', 'lightblue'][index]\n",
    "    tp.plot_timeseries(dataframe,\n",
    "                       fig_axes =fig_axes,\n",
    "                       grey_masked_samples='data',\n",
    "                       adjust_plot=adjust_plot,\n",
    "                       color=color,\n",
    "                       time_label='day',\n",
    "                       alpha=0.6, \n",
    "                       data_linewidth=0.3,\n",
    "                       selected_dataset=index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grey samples are masked out. Note the extreme values occuring due to nonlinearities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Causal discovery analysis\n",
    "\n",
    "Given this stationary dataset, the first causal question is about causal discovery. To choose the appropriate causal discovery method, assumptions that can reasonably be made must be determined. \n",
    "\n",
    "### Stationarity assumption, determinism, latent confounders\n",
    "\n",
    "The data here comes from multiple datasets (blue box in the causal discovery frame, Fig. 2 in paper), however, the datasets share the same underlying distribution and the next question is whether this system is deterministic. Given the complexity of the dynamics at this scale, it can be assumed to be a non-deterministic system. The next assumption to be made is whether or not there might be hidden confounders, that is, unobserved variables that causally influence two or more observed variables. Here, due to restricting the analysis to seasons across which stationarity can be expected, it is reasonable to assume the absence of hidden confounding, which is true in the underlying SCM. \n",
    "\n",
    "### Structural assumptions\n",
    "\n",
    "The structural assumption of the graph type then needs to be made. As the processes here are fast, contemporaneous causal effects (that is, causal influences on a time scale below the data's time resolution of 1 day) might occur. Further, here the domain knowledge that Rg is exogeneous can be enforced by not allowing any parents of Rg in the graph. These assumptions suggest using the constraint-based causal discovery algorithm PCMCI$^+$ (or other similar options, Fig. 2). \n",
    "\n",
    "To make an assumption on the maximal time lag in the causal time series graph estimated by PCMCI$^+$ (that is, the maximum over all $\\tau$ such that $X^i_{t-\\tau} \\to X^j_t$ is in the graph), data can be used to investigate the lagged dependency functions, or, as done here, domain knowledge can be used to justify $\\tau_{\\max}=1$ (in units of days). \n",
    "\n",
    "### Parametric assumptions\n",
    "\n",
    "The next hyperparameter choice for PCMCI$^+$ is about the conditional independence test, which requires a parametric assumption. Below we use Tigramite's ``plot_densities`` function to investigate the type of dependencies via joint and marginal density estimation. Here we depict the raw data as well as transformed data that achieves normally-distributed marginals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe_here = deepcopy(dataframe)\n",
    "matrix_lags = None\n",
    "matrix = tp.setup_density_matrix(N=N, \n",
    "        var_names=dataframe.var_names, **{\n",
    "        'figsize':(6, 6), \n",
    "        'tick_label_size':6,\n",
    "        'label_space_left':0.18})\n",
    "\n",
    "# Standardize data to make it comparable\n",
    "data_dict_here = deepcopy(data_dict)\n",
    "for m in data_dict_here.keys():\n",
    "    mean, std = pp.weighted_avg_and_std(data_dict_here[m], axis=0, weights=(mask_dict[m]==False))\n",
    "\n",
    "    data_dict_here[m] -= mean\n",
    "    data_dict_here[m] /= std\n",
    "\n",
    "dataframe_here = pp.DataFrame(data_dict_here,\n",
    "        analysis_mode='multiple',\n",
    "     mask=mask_dict, var_names=var_names,)\n",
    "\n",
    "matrix.add_densityplot(dataframe=dataframe_here, \n",
    "    matrix_lags=matrix_lags, label_color='red', \n",
    "    snskdeplot_args = {'cmap':'Reds', 'alpha':1., 'levels':4})\n",
    "\n",
    "# Now transform data to normal marginals\n",
    "data_normal = deepcopy(data_dict)\n",
    "for m in data_normal.keys():\n",
    "    data_normal[m] = pp.trafo2normal(data_dict[m], mask=mask_dict[m])\n",
    "dataframe_normal = pp.DataFrame(data_normal,\n",
    "    analysis_mode='multiple',\n",
    " mask=mask_dict, var_names=var_names)\n",
    "\n",
    "matrix.add_densityplot(dataframe=dataframe_normal, \n",
    "    matrix_lags=matrix_lags, label_color='black', \n",
    "    snskdeplot_args = {'cmap':'Greys', 'alpha':1., 'levels':4})\n",
    "\n",
    "# matrix.adjustfig(name=\"data_density.pdf', show_labels=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The marginal densities (red) are slightly non-normal. By transforming the variables such that their marginals are normally distributed, the resulting joint densities are reasonably linear. Here there are only small differences, but these can nevertheless affect the conditional independence tests. Therefore, conditional independence tests in PCMCI$^+$ should be undertaken with a robust partial correlation (RobustParCorr) here, which is a variant of partial correlation where the variables are first transformed to normally-distributed marginals.\n",
    "\n",
    "### Sliding window analysis with PCMCIplus\n",
    "\n",
    "The data from all sites and all of the six years can now be used to estimate a single causal graph. Alternatively, a sliding window analysis should be considered if non-stationarity of the causal graph could be present. More for illustrative purposes, a sliding window analysis is applied here that learns five causal graphs, each by combing data from two successive of the six years and across all sites (hence $5 = 6-1$ graphs, each estimated from $n=1840$ samples due to masking)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assumed links of 0: {}\n",
      "Assumed links of 1: {(0, 0): 'o?o', (0, -1): 'o?>', (1, -1): 'o?>', (2, 0): 'o?o', (2, -1): 'o?>', (3, 0): 'o?o', (3, -1): 'o?>'}\n",
      "Assumed links of 2: {(0, 0): 'o?o', (0, -1): 'o?>', (1, 0): 'o?o', (1, -1): 'o?>', (2, -1): 'o?>', (3, 0): 'o?o', (3, -1): 'o?>'}\n",
      "Assumed links of 3: {(0, 0): 'o?o', (0, -1): 'o?>', (1, 0): 'o?o', (1, -1): 'o?>', (2, 0): 'o?o', (2, -1): 'o?>', (3, -1): 'o?>'}\n"
     ]
    }
   ],
   "source": [
    "# Maximum time lag\n",
    "tau_max = 1\n",
    "\n",
    "# Conditional independence test\n",
    "cond_ind_test = RobustParCorr(mask_type='y', verbosity=0)\n",
    "\n",
    "# Significance level\n",
    "pc_alpha = 0.05\n",
    "\n",
    "# Stepsize of sliding window (this can be chosen as desired)\n",
    "window_step = 365*1\n",
    "\n",
    "# Length of sliding window (this should strive a balance between enough samples and \n",
    "# the window-length over which stationarity can be assumed, which, however, is not relevant in our example)\n",
    "window_length = 365*2\n",
    "\n",
    "# First set link assumptions to remove parents of Rg\n",
    "# link_assumptions = None\n",
    "# See the tutorial on link assumptions in the overview tutorial\n",
    "link_assumptions_absent_link_means_no_knowledge = {0: {(i, -tau):'' for i in range(N) for tau in range(0, tau_max+1) if not (i==0 and tau==0)}}\n",
    "link_assumptions =  PCMCI.build_link_assumptions(link_assumptions_absent_link_means_no_knowledge=link_assumptions_absent_link_means_no_knowledge,\n",
    "                                   n_component_time_series=N,\n",
    "                                   tau_max=tau_max,\n",
    "                                   tau_min=0)\n",
    "for j in link_assumptions:\n",
    "    print(f\"Assumed links of {j}: {link_assumptions[j]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize PCMCI class\n",
    "pcmci = PCMCI(\n",
    "        dataframe=dataframe, \n",
    "        cond_ind_test=cond_ind_test,\n",
    "        verbosity=0)\n",
    "\n",
    "method_args={'tau_min':0, 'tau_max':tau_max, \n",
    "            'pc_alpha':pc_alpha,\n",
    "            'link_assumptions':link_assumptions,\n",
    "            }\n",
    "\n",
    "# Run sliding window PCMCIplus\n",
    "summary_results = pcmci.run_sliding_window_of(method='run_pcmciplus', \n",
    "                                            method_args=method_args, \n",
    "                                            window_step=window_step,\n",
    "                                            window_length=window_length,\n",
    "                                            conf_lev = 0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code places the sliding window graphs under the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_pos = None\n",
    "            #  {\n",
    "            # 'y': np.array([0.5, 0., 0.5, 1.]),\n",
    "            # 'x': np.array([0., 0.5, 1., .5])\n",
    "            # }\n",
    "            \n",
    "graphs = summary_results['window_results']['graph']\n",
    "val_matrices = summary_results['window_results']['val_matrix']\n",
    "n_windows = len(graphs)\n",
    "\n",
    "mosaic = [['data %s' %j for i in range(n_windows)] for j in range(N)]\n",
    "mosaic.append(['graph %s' %i for i in range(n_windows)])\n",
    "\n",
    "fig, axs = plt.subplot_mosaic(mosaic = mosaic, \n",
    "                              figsize=(6, 5.),  #(6.5, 4.)\n",
    "                              gridspec_kw={'height_ratios' : [0.6/N for i in range(N)] + [0.4]\n",
    "                               } ,)\n",
    "\n",
    "reference = np.where(np.fromiter(dataframe.time_offsets.values(), dtype=int) == 0)[0][0]\n",
    "\n",
    "for j in range(N):\n",
    "    ax = axs['data %s' %j]\n",
    "    trans = mtransforms.blended_transform_factory(ax.transData, ax.transAxes)\n",
    "    ax.axhline(0., color='black')\n",
    "\n",
    "    ref_datatime = dataframe.datatime[reference]\n",
    "    len_ref = len(ref_datatime)\n",
    "\n",
    "    w = 0\n",
    "    ax.fill_between(x=ref_datatime, y1=-1, y2=1, \n",
    "            where=(np.arange(len_ref) >= w*window_step)*(np.arange(len_ref) < w*window_step+window_length), color='grey', \n",
    "                    alpha=0.3, zorder=-5,\n",
    "            transform=trans)\n",
    "\n",
    "    color = 'black'\n",
    "    for idata in range(dataframe.M):\n",
    "        data = deepcopy(dataframe.values[idata])\n",
    "        mask = dataframe.mask[idata].copy()\n",
    "        datatime = dataframe.datatime[idata].copy()\n",
    "        # print(data.shape, datatime.shape, T,)\n",
    "\n",
    "        masked_data = deepcopy(data) #.copy()\n",
    "        data[mask==1] = np.nan\n",
    "        masked_data[mask==0] = np.nan\n",
    "        if j == 0: label = \"Site %d\" %(idata + 1)\n",
    "        else: label = None\n",
    "        ax.plot(datatime, data[:,j], label = label, lw=0.3, alpha=0.6, clip_on=True) #, color=color)\n",
    "        ax.plot(datatime, masked_data[:,j], color='grey', lw=0.3, alpha=0.2, clip_on=True) #, color=color)\n",
    "\n",
    "    if j == 0: \n",
    "        for loc, spine in ax.spines.items():\n",
    "            if loc not in ['left', 'top']:\n",
    "                spine.set_color(\"none\")\n",
    "        ax.xaxis.tick_top()\n",
    "    else:\n",
    "        ax.xaxis.set_ticks([]) \n",
    "        for loc, spine in ax.spines.items():\n",
    "            if loc not in ['left']:\n",
    "                spine.set_color(\"none\")  \n",
    "\n",
    "\n",
    "    ax.set_ylabel(var_names[j])\n",
    "\n",
    "    if j == 1: ax.set_ylim(-30, 60)\n",
    "    if j == 3: ax.set_ylim(None, 30)\n",
    "\n",
    "\n",
    "for w in range(n_windows):\n",
    "    if w == 0: show_colorbar=False\n",
    "    else: show_colorbar = False\n",
    "    tp.plot_graph(graphs[w], \n",
    "        # val_matrix=val_matrices[w], \n",
    "        var_names=var_names,\n",
    "        show_colorbar=False,  #show_colorbar\n",
    "        node_label_size=8,\n",
    "        node_size=0.5,\n",
    "        arrow_linewidth=3.0,\n",
    "        link_label_fontsize=5,\n",
    "        fig_ax=(fig, axs['graph %s' %w]))\n",
    "\n",
    "fig.subplots_adjust(left=0.12, right=0.97, bottom=0.02, top = 0.93, wspace=0.2, hspace=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When compared with the ground truth from the model equations, there are errors in the individual sliding window graphs: The first graph has a false positive, other graphs have the Rg $\\to$ GPP link in the wrong direction. The last graph has a conflict link (plus-signs at the ends) due to conflicting link orientation rules within PCMCIplus.\n",
    "Such errors reflect the variance of the method due to finite samples. But they can also result from wrong assumptions. For example, the model here is nonlinear, but, after the normal-marginals transformation, linearity is still a good approximation.\n",
    "\n",
    "### Aggregated causal graph\n",
    "\n",
    "The sliding window method ``pcmci.run_sliding_window_of`` also returns an aggregated graph consisting of the most frequent link types (including absent links) across the five sliding window graphs. Edge width indicates the frequency of the link type. This aggregated graph agrees with the ground-truth.\n",
    "\n",
    "Further options are non-parametric independence tests (see tutorial on independence tests), but there is a trade-off between more sophisticated parametric models and their computational and sample size demands. Finally, in a more realistic scenario, there might be some unobserved confounders. In such cases, a method for causal discovery in the presence of hidden confounders can be used, for example, LPCMCI. Such more generally applicable algorithms will, however, generally come at the cost of a lower detection power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 260x220 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot aggregated graph (\"summary results\")\n",
    "val_tmp = np.abs(summary_results['summary_results']['val_matrix_mean'])\n",
    "val_tmp[range(len(var_names)), range(len(var_names)), :] = 0.\n",
    "cross_max = np.abs(val_tmp).max()\n",
    "\n",
    "graph = summary_results['summary_results']['most_frequent_links']\n",
    "link_width = summary_results['summary_results']['link_frequency']\n",
    "fig, ax = tp.plot_graph(\n",
    "    graph = graph,\n",
    "    # val_matrix=summary_results['summary_results']['val_matrix_mean'],\n",
    "    show_colorbar=False,\n",
    "    link_width=link_width,\n",
    "    cmap_edges = 'RdBu_r',\n",
    "    arrow_linewidth=5,\n",
    "    node_pos = node_pos,\n",
    "    node_size=0.35,\n",
    "    node_aspect=1.5,\n",
    "    var_names=var_names,\n",
    "    vmin_edges = -1, # -cross_max,\n",
    "    vmax_edges = 1, # cross_max,\n",
    "    node_ticks=0.5,\n",
    "    edge_ticks=1.,\n",
    "    label_fontsize = 8,\n",
    "    node_colorbar_label='Auto-RobParCorr',\n",
    "    link_colorbar_label='Cross-RobParCorr',\n",
    "    figsize=(2.6, 2.2),\n",
    "    # network_lower_bound=0.3,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-parametric causal effect estimation\n",
    "\n",
    "The second causal question is about total causal effect estimation ([tutorial](https://github.com/jakobrunge/tigramite/blob/master/tutorials/causal_effect_estimation/tigramite_tutorial_general_causal_effect_analysis.ipynb)). According to the QAD flow chart (Fig. 2 in paper), this estimation requires knowledge of the causal time series graph. Using the causally discovered time series graph above, the goal is to estimate the causal effect of Tair (red) on Reco (blue) at lag zero. Now all of the data ($n=1840\\times 3$ samples) is utilized, which is stationary (from the same distribution) because the winter season is masked away. \n",
    "\n",
    "### Assumed graph and adjustment set\n",
    "\n",
    "We could get the graph using ``summary_results['summary_results']['most_frequent_links']``, but if this contains unoriented or conflicting edges, the causal effect analysis does not work and you need to orient the links yourself. The format is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_graph = np.array([[['', ''],\n",
    "                            ['-->', ''],\n",
    "                            ['-->', ''],\n",
    "                            ['', '']],\n",
    "\n",
    "                           [['<--', ''],\n",
    "                            ['', '-->'],\n",
    "                            ['-->', ''],\n",
    "                            ['-->', '']],\n",
    "\n",
    "                           [['<--', ''],\n",
    "                            ['<--', ''],\n",
    "                            ['', '-->'],\n",
    "                            ['-->', '']],\n",
    "\n",
    "                           [['', ''],\n",
    "                            ['<--', ''],\n",
    "                            ['<--', ''],\n",
    "                            ['', '-->']]], dtype='<U3')\n",
    "\n",
    "# Append lag for nicer plots that include another time lag\n",
    "added = np.zeros((4, 4, 1), dtype='<U3')\n",
    "added[:] = \"\"\n",
    "original_graph = np.append(original_graph, added , axis=2)\n",
    "\n",
    "# We will assume different adaptions of this graph, starting with the unchanged version\n",
    "graph = np.copy(original_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following initializes the ``CausalEffects`` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are interested in lagged total effect of X on Y\n",
    "X = [(1, 0)]\n",
    "Y = [(3, 0)]\n",
    "causal_effects = CausalEffects(graph, graph_type='stationary_dag',\n",
    "                    X=X, Y=Y, \n",
    "                   verbosity=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph has no hidden confounders, but the effect is nonlinear, which suggests the optimal adjustment method. Let's plot this graph including the optimal adjustment set and mediators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = [('Tair', 0)] -----> Y = [('Reco', 0)]\n",
      "Oset =  [('GPP', -1), ('Reco', -1), ('Rg', 0)]\n",
      "Optimality = True\n",
      "Mediators = {(2, 0)}\n"
     ]
    },
    {
     "data": {
      "image/png": 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mZiZWrVplJYsIqWjatCnmzeN/Orx3716cP3/eShYRUjFy5Ei9j5yWLVsmalRNQm0h3bp1w7PPPssr++yzz2jvPDvgjTfeQMOGDXllH330kZWsIaSC4zgsWrSIV5aeno61a9eK1icJtQDovvI+fPiQomo7wM3NDbNnz+aV7d69m8aq7YDu3bvrLdFdunQpiouLRemPhFoAQkJC8Mwzz/DKPvvsM9qN3A6YPn06GjRowCujqNo+0A3Q0tLSRIuqSagFQtdpGRkZok8wENbH3d1dL6retWsX4uPjrWQRIRU9e/ZEeHg4r0ysqJqEWiC6deuGIUOG8MqWLl1KUbUd8Prrr6N+/fq8Mqk+LSasi26AlpqainXr1gneDwm1gBiKqtesWWMlawipcHd3xzvvvMMr27lzJy5cuGAdgwjJ6NWrl94KkKVLl6KkpMTIFeZBQi0g3bt3x9NPP80rW7ZsmWgTDITtMGPGDL1PyGms2j7QDdBSUlLw3XffCdoHCbXAGJpgoKi69uPh4aEXVW/fvh0XL160kkWEVPTu3RsDBw7klS1evFjQqJqEWmB69Ogh2QQDYVvMmDEDXl5evDKKqu0DQ1H1999/L1j7JNQiYCiq3rJli5WsIaSiTp06ePvtt3ll27ZtQ2JiopUsIqSiT58+CAsL45UtX75csI2VSahFoFevXnjqqad4ZSTU9sGbb76ptxP9zz//bCVrCClZsGAB7/jOnTs4e/asIG2TUIvE5MmTecdHjx5FWlqalawhpKJOnToYPXo0r4we0vZBv3794O/vzysTyvck1CIxfPhwuLi4VBxrNBr89ttvVrSIkIpx48bxji9fvlyxbRtRe+E4Di+++CKv7JdffuFtNGAuJNQi4e7ujueee45XRpGVfTBgwAC9/TPJ9/aB7kM6LS0Nx48ft7hdEmoR0XVaTEwM7t69ax1jCMmQy+UYM2YMr2zLli2CTSwRtkuHDh3QoUMHXpkQD2kSahEZMmQITSzZKboP6Tt37uDcuXNWsoaQEl3f//777ygtLbWoTRJqEXFyctLbsomE2j7o0aMHWrRowSsj39sHY8eO5R3n5OTg0KFDFrVJQi0yuk/Xixcv4tq1a1ayhpAKMSeWCNumZcuW6NatG6/M0uEPEmqRGTBgALy9vXllNLFkH+g+pFNSUnDy5EkrWUNIia7vd+7caVEmTRLqf/j777+xbNky7Nixg1eelZWFZcuWYePGjWa1q1AoaGLJxjl69CiWLVuGuLg4XnlcXByWLVuGP//806x2g4KC0K5dO14ZPaRti40bN2LZsmXIysrile/YsQPLli3D33//bVa7Y8aMAcdxFcePHj3C7t27zTeUEYwxxoqKipiPjw8DYPDf8uXLzW771KlTeu2dO3dOQOsJS4iLizPqdwAsLi7O7LYXLVrEa6tevXqstLRUQOsJS/jiiy+M+t3Hx4cVFxeb3faAAQN47Q0fPtzstiii/gcXFxe8//77Bs81atQIERERZrfds2dPNG/enFdmbpRGCE+XLl0wfPhwg+dGjBiBLl26mN227jh1dnY2Ll26ZHZ7hLBERkbqDU2W88EHH8DZ2dnstnWHP44dO2b2mzQJdSWmTp0KHx8fvfL33nsPrq6uZrfLcRz69evHK9N9zSasi24irXJ08zfUlICAAPj6+vLKyPe2g6urK9577z29ch8fH0ydOtWitvv37887zsvLw507d8xqi4S6Eoaiakuj6XKCg4N5x7RTtW1hKKq2NJouh3xv20RGRup9SWppNA1oH9IeHh68MnN9T0Ktg25UbWk0XU7Xrl15x7du3UJ+fr7F7RLCoRtVWxpNl6Pre4qobQtXV1e8++67FcdCRNMAIJPJ9B705vqehFqHylG1UNE0AHTu3Jk3CwyA9tSzMSpH1UJF04B+RH3p0iWUlZUJ0jYhDJXHqoWIpsvR9T0JtYCUR9VCRdOAdqumVq1a8croFdj2iIqKAsdxgkXTgH5ErVQqcfXqVcHaJyynfKxaqGi6HF3fx8bGmjWhSEJdCcYYcnJykJycjHfeeQcDBgzAgwcPLP5Ovxyhnq6E8KjVaqSnp8PNzQ0fffQR3NzckJ6eLsiXhE2aNEHjxo15ZeR7G6KwEDh9GpFNm2L5uHFwPnYMSEwEBPjWQfeez87Oxv3792vcDsfMXS9SS1CpVLh69SouXLiA5ORko6Ls5eWFVq1aITg4GA0bNjSrry+++AKzZ8+uOG7Xrh3lKbYijx49woULF5CQkICMjAxoNBq9OjKZDN7e3ujQoQM6d+4MNzc3s/oaOnQo9u3bV3E8Y8YMrFy50mzbCQu5ehVYuxbYvdu4KHt4AN26AZMnA6NHA2YMh6jVanh6euLRo0cVZdu2bcPIkSNr1I7dCrVarUZ0dDRiYmKgVCrBcVy1ryQymQwajQZNmzbFkCFDDC7lq4pjx45hwIABvPby8/PNvvkJ8ygsLMQff/yBhIQEMMZMehXlOA4cx6FDhw4YNGgQ3N3da9TnggULsGjRoorjnj174vTp0zW2nbCQ2FjgrbeAU6cAhQJQqaquL5MBGg3g6QnMng28+y7g6FijLvv06YNTp05VHM+dOxcff/xxjdqwy6GPpKQkrFq1CseOHYNSqQQAk27W8ogrKSkJ69atwx9//FGjSSHdySmNRoOLFy/WwHLCEhhjuHDhAr755htcvnwZGo3G5PFCxhg0Gg0uX76Mb775BhcuXKjRWKPuWOWFCxcoQZOUFBdrRbZbN+DMGW1ZdSINaEUaAPLygAULgE6dgBrugyjEqh+7E+rLly9jw4YNyM7ONruN8igsJiYGmzZtQnFxsUnXeXp6IiAggFdGE4rSwBjD/v37sXPnTiiVSrO/EGOMQalUYufOnThw4IDJ7eiOVRYXF+P69etm2UDUkOxsYMAA4IsvtMJr7gOSMeDWLaB3b6AGKWsNraOv6d+fXQn1pUuXsG3bthpFUlXBGENKSgo2bdqEkpISk67RdRpF1OLDGMOePXsET9x/9uxZ7Nmzx6S/JT8/P725DfK9BOTmAmFhwPnz/0bHlqBWa/+99BLw008mXaJ7z2dkZCA9Pb1G3dqNUKenp2Pnzp2Ct8sYQ0ZGBnbt2mXSDaub88OSyJ4wjfPnz4u2yiIuLs6ktyKO48j3UsMYMGUKkJBgfhRtrF3GgIkTARPytuj6Hai57+1CqFUqFbZu3SpaalHGGK5du4bLly9XW1d3EqrybDAhPFlZWTh48KCofRw8eFAvTaYhdH1fWFgolkkEAPz4I7B9u7AiXRnGgHHjgH/muYxh6FuMmvreLoT6zJkzyMzMFD0H9L59+6pdc003q7Ts3bvX4LI7IVGr1byld8Yg30tIQQEwfTqg8zWwoKjVwLVrwNdfV1lNLpfribXVhXrBggXo0qULunTpgqCgILRp06biuEuXLjh//rzB63bt2oWhQ4cKbQ40Gg3++usvwds1hFKprHbckW5W6Xj48CESExNFf0AzxnDnzh08fPiwynrkewn53/+0H7KIvfqYMeCrr6pdQWKp7wUX6o8++gjx8fGIj4/HwoUL0aRJk4rj+Ph4hISEGLxu2LBh2Lt3r9Dm4Pr165LeEGfOnKlSGOhmFQZTlradPXsWMpk0L40ymQxnq1m2RcNewlCt78vFUypSU4Fq5r8s9b2kQx9Hjx7Fiy++iJ49e6JTp054+eWXcffuXQDar3XCwsIAAH/99Rf69++Pd955ByEhIVi7dq3Zfd66dUuymxXQThLk5uYaPU9CLQyLFy/GtGnTKv5+DHHjxg3Rhz3K0Wg0uHHjRpV1yPfCMHr0aCxevBgFBQWGKyQmArdvix9Nl6NQANUMfVnqe0WNjTKTtLQ0vPXWW/j6668RFhaGnJwczJgxA99++y0+++wzg/X9/f2xZMmSio9SzOHBgweS3ayV+zT25ZpuVq6CggLKpGYGJSUlWLduHTZs2IDJkydjzpw5aNGiRcX5R48eGb+RRaKgoAC5ublGvzR1cXHhHefn55PvzeDhw4eYM2cOPv/8c8yePRszZszg530WeBlmtahUQDVfmT42Ql2vXj3s3bsXzZo1Q2FhIdLS0uDl5VXlesLnn38eDg4OcHBwMKtPlUol+RIomUyG+fPnY9euXSbVLy4uhmMNP0kl/kWlUhkU7NTUVKvYExISYvKGqPv27SPfW0B2drZhwY6LAxwcACkfgjdvAiUlRvOB2NwYtTEcHBywZ88e9OvXD0OHDsXy5cuRlZVV5Xiusb3MTKWkpMQqu32rTPk0lRCUcsEODAzEtGnTcPv2bavYYaepc6xKuWC3aNFCOySSni7dsEc5Gg1QxUYgj01EvX//fmzevBlbtmypWAC+aNEi3Lx50+g1uon2CaI6VCoVEhMTkZmZaW1TCInJz89HYmIicpRKeFRfXXiqeDjobsllsxF1QUEBZDIZnJ2dwRjDiRMnsGPHDlHH6Kz1WimXy63Sr70zcOBAnDx5En/88Qe6detmFRsouJAehUKBqVOn4tatW1i7di2aNWki7vppY3gYfzw8NhH1yJEjERsbi6FDh0Iul8Pf3x8TJ07Ejz/+KFhifl0cHR1Rt27dKldhCI1Go8GsWbPw+eefGzyfm5uL7t2788r2798Pf39/KcyrNaxYsQLffvstAGDQoEGIiopC7969K87rJuqXiu3bt8PJycnguQMHDuCtt96qOG7YsCGio6OlMq3WMG7cOMTFxUGhUGDKlCn44IMPeBPJ6NRJ2vFpAHjiCaCK3aB0FzTUNHOiqEI9atQojBo1CoBWNJctW6ZX580339Sr271792qXOplK06ZNkZeXJ+nYYVBQEBo0aGDwXEJCgl5Z79699V6NiKqpX7++QYEux8PDAy4uLiZnNhQCV1dXBAUFGT1/4MAB3nGzZs30tmcjqsfT0xPTpk3TF+hyQkOlNUguB3r1qrJKSkoK77hJkyY16kKyiNpaPPHEEybl4BAKd3d31K9f3+j55ORkvfok0jXnnXfeQZ06dYye5zgOAQEBuHLliiTLM2UyGVq2bFllHV3f1/RmJbTs2LGjSt+jdWugcWMgLU0ag9Rq4Kmnqqxiqe9rfa6PDh06GH0VFRqO49C9e/cqxyl1n6y+vr5im1UrqfJG/Ydu3bpJ+sGL7pCWLuR7YajW9zIZ8Oab2v+VAk9P4MUXq6xiqe9rvVA7ODggJCREkkkemUymt5uDLpa+AhGm4+fnBx8fH9F9z3EcfHx8qr35yPcSMnWq9otBsZHLtcmfdD5mqoxarUaaTnRPEbUB+vTpA3d3d9Fv2KeeespgSsPK0OuvtIiR6MvcfnR9TxG1iDRoAHz6qbh9yGTaIZb336+yWnp6ut6bHUXUBnB2dsbIkSNFm1CUyWRo1qwZevToUW1dev2VFl9fX/Tt21fUPvr162eSHymilpiZM7XbZokVWTMGbN4MVDMUo+t3ADXeGNsuhBrQTio+Vc2AvznIZDK4u7tj9OjRJkXsdLNKT79+/RAQECD4G1X5hKUpD4KCggK93CP0kBYZuVy7t2HjxuKI9ZIlwJNPVltN955v0KBBjefN7EaoAe0QiJBizXEcsrOzMW7cOJMmtwB6/bUGcrkcY8eORWBgoGBtMsagVqsxduxYkz5wMhRV0UNaAvz8gJMntf8rxIdoHIdHAM7PmKHd1dwEhBjutCuhBrRiPWbMGLi4uFgUYSmVSsTExODrr782OQ2rEJMKhHkoFAqMHTsW/fv3h0wmM9v3MpkMubm52L17Nz766CPExMSYdJ2uUDs5OaFevXpm2UDUkBYtgLNngREjtMfmrgaRy3HU3R0dGzXCM7/8gpycHJMuE2K40+6EGgDatm2LN954A506dQLHcSbftOX1MjMzsWnTJhw4cACMMXz66acmZUzLyMiweFKBMB+ZTIYnn3wSERER8PPzqygz9VpAm998zZo1FZvlvvbaayalQTA05EWfm0tIw4bA1q3Ajh1A+fiwqcMhMhlUHIepLVviqYIC3ElPx8OHDzF37lyTLhdiuNMuhRrQ5gYePnw4Zs2ahSeffBJ169blndcVcLlcjsDAQLz00kuYMmUKLzJWKpWYMWNGtZOVhjK6WetTZ3vG29sbU6ZMQUREBLp27aqXI1xXQJ2dndG1a1dEREQgNDSU97XjlStX8HU1e+YB+r6nNykrMXw4cO8esHcv8PTTQOWxYplMOzxS2f8tWgALF0KRlITMdu14Ta1evRrnTMh9LYjvGVFBUVER+/nnn1lISAgLDg5mISEhbPjw4SwjI4Op1Wpe3ZkzZzIAvH+///57le0vWrSIV79t27Zi/hzCRDQaDcvNzWVz5szh+X7OnDksNzeXaTSairplZWWsS5cuPD+6ubmx+/fvV9nHgAEDeNe8/vrrYv8swhRUKsauXGGT+vVjYYGBLCwggIW1bs2Of/klY1lZvKp3795lrq6uPD8GBwczlUpltPni4mLm5ORUI50wBAm1Dlu3buX9R23Tpo3Benl5eczHx4dX18/PjxUUFBhtOywsjFd/+vTpYv0MwgymT59ukn/OnDnDOI7j1R01apTRdg3drFu3bhXrZxBm0KZNG5P8s2TJEr0A7ZtvvjHa7tGjR3l1OY5jmZmZNbbPboc+LKVOnTr48ssveWVJSUn41Mgie6VSidM62/UMGDBANPsI8ejevTumTp3KK9u2bRsOHz5ssP6ZM2f0tpPr37+/aPYR4jFr1iy00xkCmTt3rtGdpP7880/ecceOHavMBWQMEmoLGDNmDAYNGsQrW7FihcGk9WfOnEFJSQmv7EkT1mAStsnixYv1MiRGRUUZnKcwdLMay65I2DaOjo7473//yyvLy8vTC9rK0fW9ucEZCbUFcByHlStX8lYOPHr0CMuXL9erq+uwqlKhErZPvXr18Mknn/DKTp8+jSNHjujVFepmJWyD/v37Y+zYsbyyFStW6C3XKyoqwl9//cUrI6G2Eq1bt8ZLL73EK1u5ciWysrJ4ZXSz1j4mTZpUsa1cOQsXLuRF1UVFRThz5gyvDvn+8ScqKoq3Oig/Px9fffUVr86pU6d4SzdlMhn69etnVn8k1AIwb948XlRdWFjIexUqLi6mm7UW4ujoqLeWNjo6mvdQjomJ4d2sHMeZfbMStkPbtm31ouqvv/6at5vUsWPHeOe7dOmitwzYVEioBaB169YYN24cr2zFihUVEwwxMTG87cY4jqPJpFrCxIkT0axZM17Zhx9+WBFV675JdenSBV5eXpLZR4jH/PnzeVF1Xl4eL6oW8i2ahFog5s2bx3NaQUFBRVSt67DOnTvTzVpLcHR0xJw5c3hlJ0+erIimaMir9tKuXTuMGTOGV/bVV18hNzcXhYWFeh/DkFDbAG3atDEYVefk5NDNWsuZPHkymjZtyitbuHAhCgsLcfbsWV45+b52YSiqXrFiBaKjo6FSqSrK5XK5Rel2SagFRDeqzs/Px+LFi+lmreUYiqqPHz+OlStXCnqzErZH+/bt8cILL/DKvvzyS+zevZtXFhISYtHeqCTUAmJogmHlypW8ySSFQkE3ay1k8uTJFYmeytFdphkaGmpyOlzi8WH+/Pm849zcXGzcuJFXZml6ZRJqgdF9FdL9yGXkyJHw9PSU2ixCZJycnPSiat0PnyZOnCilSYREdOjQAc8//zyvrKioiHf8yiuvWNQHCbXAGJpgqExERISE1hBSMmXKFL2ouhw3Nze99fZE7WHBggVGzz355JNo3bq1Re2TUIuAblRdTmBgII1P12KcnJzwvpGNTsePH0/DHrWYoKAgjB492uC5yMhIi9snoRaB9u3bG9yVetq0aSYnqiceT/7zn/8Y/KiB3qRqP/PmzdMr8/T0xMiRIy1um1RDJBo2bKhXRtF07cfZ2VlPqJ2cnNClSxfrGERIRuvWrfX2z2zWrBkcHR0tbpuEWgQYY4iOjtYr/+OPP6xgDSEl9+/fx71793hlSqXSpJ1AiMebrVu3Qq1W88ru3bvH+yrZXEioReD48eO4deuWXvmWLVusYA0hJd99953BVKfk+9rPmjVr9Mry8/Nx8OBBi9smoRaB1atXGyy/dOkSrl69KrE1hFSUlZXhu+++M3jul19+0Yu2iNpDQkICTp06ZfCcEA9pEmqBycjIwLZt24yep8iq9rJnzx6kpqYaPJeamooTJ05IbBEhFYai6XJ27tyJR48eWdQ+CbXArF+/nvclou7kwpYtW6rdrZx4PFm1ahXv2MXFhXdMD+naSUFBAX744QdeWeXluUVFRXqflNcUEmoBKSgowBdffMErCw8P5x3//fffOH/+vJRmERJw+vRpvcniIUOG8I63bt0qyMQSYVusWLECeXl5Fcccx6Fnz568OpY+pEmoBcTQfokLFy7U2wWEIqvah26+B29vbyxcuJBXlpOTg0OHDklpFiEyOTk5+Oyzz3hlw4YNw+TJk3ll+/fv19uqq0bUeN/yWsq1a9dYZGQkGzRoEG97d09PTxYZGcmWLl1a5fXZ2dnM09OTd+2wYcMYY4y99957vPImTZowlUolxc8iTGDHjh0sMjKStWvXjuendu3ascjISLZjx44qrz9y5AjvOgDsq6++YowxFhoayit/6aWXpPhJhIksXbqURUZG6t27gwYNYpGRkezatWtVXj9v3jw931+4cIFlZ2czBwcHXvl3331ntp0k1P9QVlbGAgIC9P6jl/9bv359ldcbcxhjjF24cEHv3LFjx6T4WYQJ3Lp1i8nlcoN+l8vl7NatW0av1Wg0rFevXrxr/Pz8WHFxMWOMseXLl/POubm5saKiIql+GlEN33//vdF7PjAwkJWVlRm9NiMjg7m7u/OuGTNmTMX55557jndu4MCBZttJQl2JTZs2GXSYv78/Ky0t1at/6dIl9tVXX7GkpCQ9h40dO7ainkajYW3atOGd//jjj6X8aUQ1TJw40aDvJ02aZLD+zz//zPbu3cv27t2rd82aNWsq6iUnJ+udP336tFQ/i6iG0tJS5u/vb9D3//vf/wxes2TJEnblyhX2zjvv8OrLZDJeBL5582beeVdXV7PfpEmoK1FWVsYCAwP1HLZhwwaD9Q8dOsQAMBcXlyodxhhjr776Kq/OyJEjJfhFhKkYiqrlcjm7ffu2wfrvv/++Qd8beqjrCsHKlSul+EmEiaxfv17vnm/VqpXRaLp79+5MJpPpDW1MnDiRV+/u3bt67V65csUsG2kysRIKhUJvUsjf3x8vv/yywfrlayOLi4t55RMmTECbNm14ZV27duUdx8XFWWouISABAQGYMGECr2zChAlo2bKlwfrGfB8VFQUHBwdeGfnetnn55Zfh7+/PK5s3bx4UCoXB+o8ePYJGo9HbEEQ31WmzZs1Qr149Xpm5vieh1mHcuHEIDAysOJ4/f36VDjNERkYG/v77b15ZcHAw7/jevXvIysqy0FpCSObOnVux7l0ulxvMhlaOMd9fvXoV+fn5vDJd35NQ2xYODg48X7dq1Upv/9PKFBYW6pW5urrixIkTvK9POY4TzPck1DpUjqpbtmxpNJoGDDsM0C7FWb58OTQaTUVZUFCQ3scvdMPaFpWj6ldeecVoNA0Y9/2KFSuwY8cOXpluRH3lyhW9nX8I61I5qq4qOAMM+z4/Px8LFizApUuXeOW6vo+NjTXPQLMGTGo55WPVxsamy1m0aJHeGJSvry/bt2+fwfodO3bk1V2yZIkI1hOWcOvWLebk5GR0bLqcAQMG6Pm+d+/e7ObNm3p1Hz58qFf37NmzYv0EwkzWr19f5dg0Y4ypVComk8n0/Dl16lSWl5enV//XX3/l1fPw8GBqtbrGtlFEXYni4mLcuXMH8fHxeOONNxAQEIBLly4hIyODFx2Xo5vXYfLkyUhISND7Iq0cwZ6uhLAwBjy6DyTtRIB6P3Z98wpaqvYBSTu15QY++a/se2dnZ3zxxRc4fvw4b9isnAYNGqBZs2a8MvK9baBhDPnKMtzPL0aPoSMxd+ly3C9QIuOREqVq/XteVwv8/Pxw4MABrF271uAOPrr3fEFBAW7fvl1jO43H93ZCQUEB4uLiEB8fr/cZ6NGjRyvycsjlcvj7+yMkJAQBAQGQyWQVN2urVq2watUqhIWFVdlXcHAwb3diGvqwMtnxwO21wL2fgbLcfwplCHfjgFgG4J8b0qEu0PxFIGAaUE+7AUC5759++ml8++23epNRugQHB+P+/fsVx+R768EYQ9ojJRJzi5BRpISm0nPYo21XXMj4d47BVSFHc08XtKjrCheFvMLvMpkMb731FhYuXAgPDw+jffn7+8PT05OnLXFxcWjVqlWNbLZboS4uLsahQ4dw8eJFANBLlKR7rFarcfv2bdy6dQseHh545plnkJOTgw8//BDvvfcenJ2dq+1T9+n6999/Izc31+DWTYSI5F4Bzk4FMmMATgEwVaWTGu1LamXKcoG/vwNurwYa9EJx0Aq4urpi7dq1eOGFFwzuj6lL165dsX379opjEmrrkFJQggsZeShRacBB39W6x0UqNa5lFeJaViGa1XFBcnIKQkNDsWbNGpN27eE4Dl27dsWff/5ZURYXF4cXX3yxRnZzTFeR7ICrV69iz549KCkpsSiTnY+PD8aPHw83NzeT6hcVFcHDw4P36nT06FHaoksqNCog4WPgyicAGMDMyA/NyVFSCigDZsOz58eAzLRYZ//+/XjmmWcqjh0dHVFQUCDINk1E9ShVasSn5yGlUGl2GxyAotxs9G3dHM3qupt83f/93//h888/rzgOCwvDkSNHatS33Y1RR0dH47fffkNxcbHF6UbT0tLw3XffITc316T6rq6uaNu2La+MIiuJUJcAJ0YCCR9pI2hzRBoAmBrODmp43lumbU9t2uoN3bep0tJSXLlyxTwbiBrxqEyFP+9lIdUCkQa00bZL3Xo4n16AG1mGV/0YwtASvZpqj10JdXR0dI2fZFXBGEN+fj42bNigt3bWGLo37OXLlwWzhzCCpkwrqin7oP9yay5M296JUdr2q6FRo0bw9fXllZHvxaeoTI0T97NQrFIL5nkAuJJZgJsmirXuPZ+bm4vk5OQa9Wc3Qp2YmCioSJej0WhQWFiI33//3aSnZJMmTXjHlScZCJFI+BhIPYiKyUHB0ACpB4CET0yqTb6XFsYYzqXmoESlEVSky0nILMDDouqjdF2/AzX3vV0IdUlJCbZv327SpI85aDQa3L9/H2fOnKm2rrs7f2zL2IcThEBkntUKtSi3KrTtJiwCsqrfZZx8Ly23cx4hq7hMNM8DwLmUXJQZWMZXGVdXV72ymvreLoQ6OjoahYWFom+BdeTIERQVFVVZh25WiTn3GiDSA7oCjtP2Uw3ke+lQqjS4klkgfj9qDW5kV+1HmUymt+Cgpr63eHleSkoK1qxZg5MnTyI7OxuOjo4ICgrClClT0Lt3byQlJeGpp56Ci4tLRUTLGEOjRo3w2muvYcSIEQC0CXDi4+N5CW2cnJwwYMAAREVFmbT8zRBlZWU4f/68JPsUajQaxMXFoU+fPkbr6K65pJtVRDL/AnIkmKxlaiA7Vttfg+5Gq5HvpeNuXhFvfbRYMACJuUVoW98DcpnxgMDDw4OXH0bSiPrmzZsYNmwYSktLsW7dOsTGxuLQoUMYNmwYXn/9dRw/fryi7p49exAfH1/x780338QHH3yAmJiYijoRERG8Or///jvi4uIQFRVlto0JCQlQKi2b7TUVxhj++uuvKh8KFFUJg0mTtzdXatdJSwGn0PZXBeR7YajO94wx3M6xbNfvmlCmYXhQUFxlHUt9b5FQL1iwAL1798bixYvRsmVLyOVy1K1bF8OHD0dUVBQvDWBlOI7D0KFDUadOHVy7ds1o+76+vggLC8PVq1fNtjExMVG0sWlDFBYWVpkVj25WYfjiiy8waNAgnDp1ynAFxrQTiLyPWUSEqf6ZsDQO+V4YRowYgWnTpuHu3bsGzxeUqqCsZtxYSDgADx9VHQxa6nuzw420tDTEx8fzPomuzMiRIwEASUlJeueKi4uxfft2FBYWokePHgavV6vVuHHjBg4dOoRBgwaZayYePHggybBHZS5fvqy3FKsc3Q0uCwoKcPPmTSnMqlVkZWXh8OHDOHz4MAYOHIioqCj+kFNxMqDMNN6AGCgzcffaGZTK6xk+rfNml5aWRr43g7y8PKxbtw4bNmzA5MmTMWfOHLRo0aLifE5J9cslhYQByKqmT6sKNQA0bty4oiwmJgZvvPEGAK3Qent74/vvvweg3ZlXJtMG8HK5HE888QS+/PJLtGvXruL6tWvXYtOmTQC0ry8NGjRAeHg4Zs6caZaNpaWlJn+MIhQymQxffvkldu/ebVJ9pVKJ1q1bi2xV7cagYOdctIotzwwfg2u3HphU98SJE+R7C1CpVAYFO0+pMvh5uJgUlamh0jAojIxTW02oGzZsCABIT0/HE088AQDo2bMnzp8/DwDYtm0bvvnmm4r6u3btgp+fX5VtTps2rULohaC0tFSwtmpC5eThhHRUFuyPXuuDntYwgkn3yk1oqSzYkyZNwoiINwGPBtLbodFAIZMbPGe1MWpfX18EBQXht99+M7cJghAchUIBf39/NPH2srYphMR4enqiZcuWqGODSc6sOpn46aef4uTJk5g/fz4SExPBGENhYSF27NiBlStXwtvb25LmLcbZ2VnSicRyqtodghAHhUKBadOm4fbt21izZg2a+1tpSIEzHFER4lG/fn0sXrwYiYmJeP/99+Hl6WkVOxxkxuXUakMfgDYP8549e7Bu3TpERkbi4cOH4DgOrVu3xquvvooXXngBGRkZlnRhEQqFAvXq1ZN0b0KNRoNFixZh69atBs8/fPhQb6Lx6tWrCAgIkMK8WsPChQvxySefQKFQYMqUKZgzZw6aN2/+b4V6XY1fLCIXY88AzoZfu3/99Vfe1m6+vr5ITEyUyrRaw5NPPonTp0+jfv36mD17Nl5//XXeGvW6Tg6Sjk8DgLujvMp11LrDsLIqRN0QFod+3t7emDt3LubOnWvwvJ+fH27cuFFtOz/88IOlphikadOmyMnJMbhDi1g0bdpUbyfqcgw9uJo3b260PmEYZ2dnTJs2TV+gKyo0BFyaAMUp0hnl4gsHDx+jp9PT03nHvr6+5Hcz8Pb2xuLFi/UEuhwvZ2n/m3IA6rtUna5WNwmTofwfVVHr39EDAwNx4cIFyfqrV69elRsBpKTwhaNu3boGcwEQVfPBBx/obRash99w4PY6adZScwptf1Wg63tjSziJqtm6dWuVvnd1kMPNQY5HZdJM6jMAjd2cqqxjqe9rfa6PNm3a6I0PiUmPHj2qHBfXdVhNn6yElmpFGgBazZD2g5dWM6qsQr4Xhup8z3EcArxM28xDCJzlMvi4V53iwlLf13qhlslk6N7deP4FIXFyckKnTp2qrKP7CkRRlYh4tgMaPSX+Z+ScAmg8EPBsW2U18r10NPN0MbqmWWgCvNwgqyI4Kysr0xvypIjaAD169EDDhg1FXwHyzDPPVLu1EkVVEhO6ShqhDl1VbTXyvXQ4yGTo7K2/K7iQcAA8HBVoWU30npaWpvd1NEXUBlAoFBg9erRoQs1xHNq2bYugoKBq61o6qUDUkDqBQPBycfsI/hLwqHrVDmOMImqJaVrHBU3cnSBmeNbNp26Vqz0A/Xue4zjeF92mYBdCDWi3QipPqSokHMfB29sbw4YNM+lBQBNKViAgEmg5FRDjlm05FQiIqLZaXl4eiov5GdboIS0uHMeha+O6qOOkEEWsQ3zqwtOEFSa697y3t3eNV/vYjVADQFBQEEaPHg2ZTCZIdM1xHHx9fTFx4kST82XT668V4Dig22ogcHp5gaUNav+n1Rvads14QAP0kJYCR7kMfZvWR12Bluxx//wL9amLpnVcTLpGiOCs1i/P06VDhw7w8vLCjh07kJlpXna1cpHv1asX+vfvb/LTUYhJBcJMOBkQshKo3w2IfRNQFZq3EzknBxTuQPAK4IkJJu8eo/v66+rqijp1xB1DJbQ4ymXo17Q+rmUV4Gb2I4sSNrk5yhHSuC7qVbNuujJCDHfanVADWnGMjIzEqVOncPr0aSiVSnAcV206VJlMBo1GAz8/PwwZMgQ+PsY/bjCEEJMKhAVwHOD/CuAzGIh/F7j30z9JlEz5GEqmFfvmLwFdPwOca5YewVBUZY30BvaKXMahQ8M68PVwwcWMPGQXl5kk2OV1FDIOreq5oVU99ypXeBiCImoLkMvl6NevH3r16oVr164hPj4eycnJRjPueXl5oVWrVggODq7IHFhTrl+/zjuWyWQ1nlQgBMClEdBrE9D1c+DORuDez0DeZUBjIKewzAHwDAKavwj4T9J+8WgGur6nB7R18HJ2wJPNGiBfWYbEvCKkFSqNfhij4Dh4uTighacrmrg7VztpaAwhfG+3Ql2OQqFAUFAQgoKCwBjD1q1b8fbbb1dEvi1atMDRo0erXXZnCseOHeMdd+rUiRI4WRPnhkC7/9P+05Rhwftv4Iefd0AbQ3GY8OIIfLRkpVasLUTX9yEhIRa3SZhPHScHdPL2RCdvbXrSpwYOROKdOxXnv175DZ57dqjFbz0FBQWIjY3llZnje1KJSnAcB5lMxtuVxsPDQxCRBoA///yTdzxgwABB2iUEQOaArEdy3H3wbz6OrEdyQUS6oKAA586d45WR720HhUyGjNRUPLh3r6JMU6oUZGjq5MmTvPz0crkcffv2rXE7drXqw5oUFhbSzWqnREdHC3KzEo8fusFZSEiIwURS1UFCLRGnTp2CSvVv3gmZTEY3q52gO+wRHBxMKz7sBF3fmxuckVBLhO6TNTg4GJ5WSnBOSAsNedkneXl5iIuL45WRUNs4dLPaJ3l5eXqTSeR7++DEiRO8PPgODg7o3bu3WW2RUEtAfn4+3ax2ysmTJ3k3q0KhMPtmJR4vdIOzbt26wc3NvPSrJNQSoDvzq1Ao0KdPHytaREiFoZtVyvzohPUQ8i2ahFoCdCcUQkND6Wa1E4SaTCIeL7Kzs3Hx4kVeGQm1jUPj0/ZJTk4O4uPjeWXke/vgxIkTvHQRjo6O6Nmzp9ntkVCLTGpqKt2sdsrBgwf1btZevXpZ0SJCKvbt28c77tmzJ1xcTMu2ZwgSapFZv349bzLJ1dWVblY7Yd26dbzjPn36WHSzEo8HBQUF2LJlC69s0KBBFrVJQi0iarVa72Z98cUXaddxO+DWrVs4evQor2zKlClWsoaQki1btqCwsLDiWCaTYeLEiRa1SUItIocOHcK9SvkDACAyMtJK1hBSsnbtWt5x/fr1MXr0aCtZQ0gFYwyrV6/mlT377LPw8/OzqF0SahHRdViXLl0oa5odUFJSgg0bNvDKJk2aZPIuQMTjy/nz5/XmpCIiqt+qrTpIqEUiKSkJe/bs4ZVFRERQsng7YNu2bcjKyuKVTZs2zUrWEFKyZs0a3nGzZs0wePBgi9sloRaJ77//njeJ6O7ujpdeesmKFhFSoXuzhoWFoVWrVlayhpCKvLw8vUnEadOmQS6XW9w2CbUIqFQqfPfdd7yy8ePHm5XekHi8uHbtGk6cOMErE+LVl7B9fvzxRxQVFVUcKxQKwSaQSahFYN++fbzNBwC6We0F3Wja29sbI0aMsI4xhGQYmkQcPnx4jfdVNQYJtQjo3qzdunVDly5drGQNIRXFxcXYtGkTr2zKlCmC7RBE2C5nzpzB5cuXeWVCBmck1AJz79497N+/n1dG0bR98NtvvyE3N5dXNnXqVOsYQ0iKbnDm7++Pp556SrD2SagFZvXq1bzPhj09PTF27FgrWkRIAWMM3377La9s8ODB8Pf3t5JFhFQ8fPgQv/zyC68sIiICMplw8kpCLSAPHz7EypUreWUTJkwwOwct8fiwf/9+nD17lldGb1L2wdKlS1FSUlJx7ODggEmTJgnaBwm1gCxduhSPHj2qOJbJZJgxY4YVLSKkgDGGefPm8cr8/f3x7LPPWskiQipSUlL03qTGjx8Pb29vYTtiBGOMsYSEBBYeHs46duzIAFT8c3V1ZeHh4ez//u//qrw+OTmZOTs7866dNGmSRNYTlrB582YWHh7O/Pz8eP7z8/Nj4eHh7Mcff6zy+t9//513HQD2v//9TyLrCUuYPXs2Cw8PZ66urjz/dezYkYWHh7OEhIQqr3/99dd51ykUCnbnzh3B7SSh/geNRsO6du2qd8OV/9u+fXuV10+fPl0ShxHCk5KSoveQLf/n7OzMUlNTjV6rUqlYu3bteNe0adOGqVQqCX8BYS7btm0zes8HBwczjUZj9NrExETm4ODAuyYyMlIUO0moK7Fr1y6DDuvcubNBh509e5bNnDmTxcbGSuYwQhxmzpxp0PezZs0yWH/VqlXsm2++YRs2bNC75tdff5XYesJc1Go169Spk0Hf79692+A1b731Fjt48CCbMmUKr76TkxN78OCBKHaSUFfCWFRtLJo+ePCgQQeL6TBCHAxF1VVF0++9955B33fq1Imp1WqJrScswVBUXVU03a1bN4O+nzlzpmg20mRiJTiOw4cffsgr69y5M4YPH26wfuWcs5V57bXXLE5rSEiLj4+PXgra1157DY0bNzZY35jvFy1aJOiyLEJ8hg8fjk6dOvHKPvzwQ6MJ1Az53tXVFe+//74o9gG06kOPZ599FsHBwRXHUVFRRh1WeYVHZU6ePKmX74Gwfd57772KVKTOzs549913jdY15vuffvoJiYmJothHiINMJuMFaMHBwRg6dKjR+oZ8r9Fo8O2336KgoEAME0modakcVVcVTQPGo6pLly4hOjqalz2PsH0aN26M1157DUDV0TRg3PfHjh3D1atXRbGPEI/hw4ejc+fOAKqOpgEYFOOSkhIcOHAAKSkp4hgo2qDKY4xGo2HBwcHVrvSIiooyOLZ1+fJlaQwlBCc1NZXVq1evypUejDHWt29fPd+PHz+eZWVlSWQpITTbt2+vdqWHUqnU87ujoyNbvHgxKysrE802hTjy//ihUqmQkZGBtLQ0ZGZmYtKkSXB0dMTx48fh4+MDHx8fuLu78560aWlpFf/f0dER8+fPx3vvvQcHBwdr/ATCXErzgJwLQE4cGj96gCMrBqJx8jIgtyng1RXw6gw4evIuqez7Ro0aYfXq1ZQl7zGDMSAlBYiP1/57+HA4AgKewLvvcmjdGujSBejQAXBy+veajIwMXhshISHYuHEj2rdvL6qtdi3UjDEkJycjNjYWly9fhlqtBoCKyaDY2Fgw7coYAECDBg0QGhqKjh07wtnZGampqQCAJ598EqtXr0br1q2t80OImqMpA5J3AzdXAelHoA2OZAAnR2cZgJsAmBqABgAHNH4KCHwN8H0OkDkgNTUVHMchMjISn376KerWrWvFH0PUhNxc4IcfgP/+F7h+XVsmlwMyGQdAO6lYVqYtd3ICxo0DIiKA7t1Rcc97eHjgk08+wfTp0wXZGKA6OMYqZRCyI7KysrBr1y7cv38fMpmsRuPJCoUCYWFh+PjjjzF27FhMmDCBtth6nEg5CJx9FShKAjj5P4JcDeX1XP1Q2H4lej8fhbVr16J79+7i20sIgloNrFgBzJ0LlKfmMEX9FApApQL69gXGj9+Nw4f/h6+++gq+vr7iGlwJuxNqxhhiYmJw9OhRMMYsmvCrX78+xo0bh/r16wtoISEaZYXAuenA3R+gnUc3x/cylKk0QIvxcOi5GnBwF9hIQgxu3dJGxrGx5rehUAAcV4zFi13w9tuAlLGZXQk1Ywx79+5FrCXeqgTHcXBycsKECRPQpEkTQdokREKZDRwdBOReNC2Crg5ODtTtBIT9ATjVs7w9QjRiY4FBg4D8fG1ULQTTpgGrVgFSLZm3K6Het28fzp07J2ibHMfB0dERU6ZMET5jFiEMZYXA4b5A7mVhRLocTg7U7QgMOgkoKJWtLXLlCtCrF/DokXAiXc706YBO4jzRsJt11FeuXBFcpAFtlF5aWorffvsNKpVK8PYJAYifDeRcElakAW17OReBuNnCtksIglIJPP+8OCINaCcjf/tN+HYNYRdCXVBQgN27d4vWPmMMmZmZ+PPPP0XrgzCTlP3A7TUwbzzaFDTA7dVAygGR2ifMZf584OZNcUQa0I5RT50K/LMQRFTsQqhPnDiB0tJS0fuJiYlBXl6e6P0QJsIYcP5NiP9nLgNi3zRtCQEhCffvA198AYj5cTBjQGEh8PHH4vVRTo3+gsPCwhAUFIQuXbqgS5cu6Ny5M7p27Yrx48fb7GezJSUluHDhAqQaij9//rwk/RAmkHYYKLwN8aLpcjRAwa1/1mMTtsCqVdKsylCrgfXrAbHjsxqHGgsXLkR8fDzi4+Nx4cIFHDp0CB4eHpgxY4ZN5ra4ePGiZGPHjDGcO3fOJv871Dbu3btXvV9vfqOd8JMCTgHc+EaavuycO3fuVHm+rEwr1GINeeiiVGo/oBETi98JGzRogLFjxyI5ORm5ubnIzMzE7Nmz0bt3b/Tp0wcLFizgJbA5deoUnn/+eXTp0gVhYWHYvHlzxbnDhw9j1KhR6Nq1KwYPHoyNGzdaLHr37t2T9GMUpVKp95kpITzr169H+/btsXnzZsOCzRiQcUz4CURjMBWQ8ScNf0jAxIkTMWjQIERHRxs8f+WK+BFuZWQy4Ngxcfuw+BPy1NRUbN68GUFBQahbty5efPFFtGjRAgcPHkRZWRk++OADLFiwAMuXL0diYiIiIyMRFRWFESNG4Pr163jllVfQvHlzODg4YObMmVi2bBnCw8Nx48YNTJ8+HQAs2tH3wYMHkg17lBMdHQ0PDw9J+7Q3bt++jZs3b2LChAlYtGgR5s+fjxdffBEKxT9/0oV3gLJ8aY0qy0f0H7/hEfOsvi5hNjk5Obhy5QoOHz6MgQMHIioqCn369Kk4L8LiripRq4GYGHH7qNE66rCwMGRlZcHBwQEqlQplZWVo3LgxBg0ahIiICDx48ADjxo3D2bNn4eamXVeamJiIp59+GmfOnMGPP/6I6Oho/PzzzxVtXr9+Hd7e3liyZAmUSiW+/vrrinM//vgjfvjhBxw4YN6MulKpxJIlS8y61lxkMhnOnTsn6ioTwjCtWrX6V7DT9gInRkhuQ7sP/XDtVpLk/do7lQX7rbe0Qx/l+TqkIj8fECs+q/HQR1RUFM6fP4+zZ89i1qxZyMvLQ//+/eHl5YWkpCSo1Wr0798fISEhCAkJwQsvvABHR0c8ePAAGRkZel/wtWnTBvXq1UNWVhaaNm3KO+fn54fk5GSzf5wUKz0MoZZqcIzgUR5ht2/fHnsOnLSOEYzmJ6zB4cOH0bdvXwwcOBDJydesMgJlZC8JQTB76MPR0RGvvvoq8vLyMH36dGzZsgWNGzeGs7Mz/vrrr4qMUqWlpXjw4AGaN28OHx8fHD9+nNfO77//jvr168PX1xf379/nnXvw4AEaNmxorolW2xKJEjRZj/r162Py5Mno36MRcMkaFpDvrUW7du0wdepUHD7cyir9i5lEz+Ix6pkzZ+LcuXN4++238euvv6J58+ZYsmQJZs6cCblcjiVLluDIkSM4fPgwhg4dilWrVmHHjh147rnncO3aNSxZsgRffvklRo8ejfHjx2P//v0VY9Tr1q3DmDFjzLbN2dm5xpnxhKBu3bq0Z6LI5OXl8XbaqF+/PmbPno3XX39dOz+QdtQqdjVu1BAFJSTWYpKeno6ySuMa7dq1w4IFC/D8889DLpfj8mXpbVIoAE8RpyZqPEY9Y8YMjBo1ilf+4MEDjBgxAsOGDUNERASWLl2Ks2fPQqlUomPHjpgzZw4CAgIAAGfOnMEXX3yBO3fuoH79+oiIiMDo0aMBAEeOHMG3336LxMREeHl5YcyYMZg6dapF+V5Xr16N9PR0s683h7Fjx6JNmzaS9mlvREVF4aOPPtIX6HJKc4GtXtIb9nwO4FhX+n7tiL59+yI6OlpPoMvZvh3QkSjR6dQJuHBBvPZrfVKmPXv2ID4+XtKoetasWahTp45k/dkjK1asQFFRkb5AV2bnE8Cju9IZ5fYEMLzqNb6E5URERCAsLExPoMtJSgJ0prtERaEAXn1VO4EpWh/iNW0btG3bVrC0ptXBcRwaNWpEIi0Bb775ZvWVmo8Frn0uzVpqTg40N3+YjjCdNWvWVHne1xfo2lUb4UoRn6lUwMiR4vZR63N9+Pv7o149afIFM8bQs2dPSfoiTCBwunSrMJhG2x9hdTgOmDVLGpHmOCAgQJvvWkxqvVBzHIdevXpJ0o+7uzvatWsnel+Eibg1A5q9IP5n5Jxc249bM3H7IUzmhReAxo3FT+zPGPDuu+LnFan1Qg0AXbt2RbNmzURdNscYw4gRI/79Mo6wDYK/BhQeEG/ZHAco6gDBK0RqnzAHJydg0yZxo2qFAujXD/jPf8Troxy7EGqO4zBy5EgoFApRxJrjOISGhqJly5aCt01YiEtjoMf30O4yLgYM6PEd4NJIpPYJcwkP1+7CIkZULZNpHwY//CDNdlx2IdSAdm3zuHHjIJPJBBVrjuPg7++PwYMHC9YmITBNRwGdPhGn7U6fatsnbJLly7WCLaSYymTaaHrPHqCZRKNdtX55ni737t3DTz/9BJVKJciSvbZt22LUqFE05PE4cP1LIO5tAHIAFqwE4eTalSRdlwNtZgllHSESSiUwfjzw+++Wt6VQAM7OwN692mEPqbA7oQaA3Nxc7N69u9q8tsaQyWSQy+UIDw9HcHAwfTL+OJF+HDgzGXh0D+ZtKCAD3JoDPTcC3hLeqYRFaDTAunXA228DpaXaJXU1geO0E4fh4cDatUDz5uLYabR/exRqQDv5d/nyZZw8eRKZmZkmf2oul8vRoUMHhIWF0XrpxxVVMXBtGXBjBVCa/W+EbIzy8471gNZvAm3fBRQu0tlLCEZSEjB3LvDzz/9m16tKARUKrai3aQPMmwe89JI0O8foYrdCXQ5jDElJSbh48SIePHiAzMxMPcF2cXFBkyZNEBgYiI4dO8LFhW7SWoG6FEjeBdzfCmT9ZfgrRrcWQP3u2uV3vs8BckeprSREIDtbOxG4fz9w/jyQlcU/r1Boxbl3b2DiRKBHD+sIdDl2L9S6qNVq5ObmoqysDDKZDK6urnB3d7e2WYQUlOUDRUmAphSQOQKufoADvTXZA2lpQGamNnp2dQVatAAcbeiZTEJNEARh49jN8jyCIIjHFRJqgiAIG4eEmiAIwsYhoSYIgrBxSKgJgiBsHBJqgiAIG4eEmiAIwsYhoSYIgrBxSKgJgiBsHBJqgiAIG4eEmiAIwsYhoSYIgrBxSKgJgiBsHBJqgiAIG4eEmiAIwsYhoSYIgrBxSKgJgiBsHBJqgiAIG+f/AViuHY8gK+IGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt = causal_effects.get_optimal_set()\n",
    "if opt is False: print(\"NOT IDENTIFIABLE!\")\n",
    "print(\"X = %s -----> Y = %s\" % (str([(var_names[var[0]], var[1]) for var in X]), str([(var_names[var[0]], var[1]) for var in Y])))\n",
    "print(\"Oset = \", [(var_names[v[0]], v[1]) for v in opt])\n",
    "print(\"Optimality = %s\" %str(causal_effects.check_optimality()))\n",
    "print(\"Mediators = %s\" %str(causal_effects.M))\n",
    "\n",
    "special_nodes = {}\n",
    "for node in causal_effects.X:\n",
    "    special_nodes[node] = 'red'\n",
    "for node in causal_effects.Y:\n",
    "    special_nodes[node] = 'blue'\n",
    "for node in opt:\n",
    "    special_nodes[node] = 'orange'\n",
    "for node in causal_effects.M:\n",
    "    special_nodes[node] = 'lightblue'\n",
    "\n",
    "link_width = None\n",
    "\n",
    "fig, ax = tp.plot_time_series_graph(\n",
    "    # graph = pcmcires['graph'],\n",
    "    # val_matrix=pcmcires['val_matrix'],\n",
    "    graph = causal_effects.stationary_graph,\n",
    "    # graph = causal_effects.graph,\n",
    "    link_width=link_width,\n",
    "#     link_attribute=graph_attr,\n",
    "    cmap_edges = 'RdBu_r',\n",
    "    arrow_linewidth=3,\n",
    "    curved_radius=0.4,\n",
    "    node_size=0.15,\n",
    "    special_nodes=special_nodes,\n",
    "    var_names=var_names,\n",
    "    link_colorbar_label='Cross-strength',\n",
    "    figsize=(4, 3),\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph shows $X$ (red) and $Y$ (blue), the mediator (light blue), and the adjustment set in orange. This adjustment sets blocks all non-causal paths between $X$ and $Y$. \"Optimality\" refers to whether the optimal adjustment set has the smallest asymptotic estimator variance across all adjustment sets (see [Runge 2021](https://arxiv.org/abs/2102.10324)).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interventional ground truth\n",
    "\n",
    "Before estimating causal effects from the observational data, because here we have the structural causal model, we can generate ground truth of causal effects by actually intervening in the model. Let's do this. Here we only consider one site because they have the same distribution.\n",
    "\n",
    "We will consider a number of different interventions in ``dox_vals`` and get an interventional dataset for each intervention. We only want to model interventions in Rg at time $t$, while past values of Rg should be observational. Hence, we need to model both the observational ``modeldata`` and the interventional model ``intervention_data``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dox_vals = np.linspace(-2, 50, 30)\n",
    "\n",
    "modeldata = np.zeros((T,N))\n",
    "intervention_data = np.zeros((len(dox_vals), T, N))\n",
    "random_state = np.random.RandomState(11)\n",
    "noise = random_state.randn(T, N)\n",
    "noise[:, 1] += 0.25*random_state.randn(T)**3\n",
    "\n",
    "for t in range(1, T):\n",
    "    \n",
    "    # Observational data\n",
    "    modeldata[t,0] = np.abs(280.*np.abs(np.sin((t)*np.pi/365.))**2 + 50.*np.abs(np.sin(t*np.pi/365.))*noise[t,0])\n",
    "    modeldata[t,1] = 0.8*modeldata[t-1,1] + 0.02*modeldata[t,0] + 5*noise[t,1]  \n",
    "    modeldata[t,2] = np.abs(0.2* modeldata[t-1, 2] + 0.002*modeldata[t,0] * modeldata[t,1] + 3*noise[t,2]) \n",
    "    modeldata[t,3] = np.abs(0.3*modeldata[t-1,3] + 0.9*modeldata[t,2] * 0.8**(0.12*(modeldata[t,1]-15)) + 2*noise[t,3])\n",
    "        \n",
    "    # Interventional data where Tair is intervened \n",
    "    # and this intervention propagates along the mediator GPP and then affects Reco,\n",
    "    # while variables not on causal paths are taken from the observational data\n",
    "    for i, val in enumerate(dox_vals):\n",
    "        intervention_data[i,t,0] = modeldata[t,0] # stays the same   #np.abs(280.*np.abs(np.sin((t)*np.pi/365.))**2 + 50.*np.abs(np.sin(t*np.pi/365.))*noise[t,0])\n",
    "        intervention_data[i,t,1] = val # Intervened variable at time t    #0.8*modeldata[t-1,1] + 0.02*modeldata[t,0] + 5*noise[t,1]  \n",
    "        intervention_data[i,t,2] = np.abs(0.2* modeldata[t-1, 2] + 0.002*modeldata[t,0] * intervention_data[i,t,1] + 3*noise[t,2]) \n",
    "        intervention_data[i,t,3] = np.abs(0.3*modeldata[t-1,3] + 0.9*intervention_data[i,t,2] * 0.8**(0.12*(intervention_data[i,t,1]-15)) + 2*noise[t,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Remark:__ Ensembles of interventional time series data can also be generated with the function ``structural_causal_process_ensemble`` in the toymodels module."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumed parametric model and estimation of causal effects\n",
    "\n",
    "For an introduction on causal estimation we refer to the review paper and the [tutorial](https://github.com/jakobrunge/tigramite/blob/master/tutorials/causal_effect_estimation/tigramite_tutorial_general_causal_effect_analysis.ipynb).  A nonlinear adjustment as given by \n",
    "\n",
    "\\begin{equation}\n",
    "\\mathbb{E}\\left[Y ~\\vert~ do(\\mathbf{X} = \\mathbf{x})\\right] \n",
    "\\, = \\,\n",
    "\\mathbb{E}_{\\mathbf{z}}\\left[\\mathbb{E}\\left[Y ~\\vert~ \\mathbf{X} = \\mathbf{x}, \\mathbf{Z} = \\mathbf{z}\\right]\\right] \n",
    "\\end{equation}\n",
    "\n",
    "requires the choice of a statistical or machine learning model.\n",
    "Here two approaches are compared: First, covariate adjustment with a linear regression [sklearn's LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)  (labeled LinReg, colored grey) and, secondly, covariate adjustment with a multilayer perceptron [sklearn's MLPRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html) (abbreviated and labeled MLP, colored red), also known as feedforward neural network.\n",
    "\n",
    "Let's wrap the estimation including initialization with the graph, $X$ and $Y$, intervention values ``dox_vals``, bootstrap confidence interval estimation, and plotting into a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_predict_plot(X, Y, graph, dataframe, estimator, dox_vals,\n",
    "                     data_transform=None, graph_type='stationary_dag', adjustment_set='optimal', \n",
    "                     confidence=False,\n",
    "                     boot_samples=3,\n",
    "                     seed=None,\n",
    "                     fig_ax=None,\n",
    "                     label=None,\n",
    "                     figsize=(4, 3),\n",
    "                     verbosity=0):\n",
    "\n",
    "    causal_effects = CausalEffects(graph, \n",
    "                        graph_type='stationary_dag',\n",
    "                        X=X, Y=Y, \n",
    "                       verbosity=verbosity)\n",
    "    causal_effects.fit_total_effect(\n",
    "        dataframe=dataframe, \n",
    "        mask_type='y',\n",
    "        estimator=estimator,\n",
    "        adjustment_set = adjustment_set,\n",
    "        ignore_identifiability=True,\n",
    "#         conditional_estimator=conditional_estimator,\n",
    "        data_transform=data_transform\n",
    "        )\n",
    "\n",
    "    # Fit causal effect model from observational data\n",
    "    if confidence:\n",
    "        causal_effects.fit_bootstrap_of(\n",
    "            method='fit_total_effect',\n",
    "            method_args={'dataframe':dataframe,  \n",
    "            'mask_type':'y',\n",
    "            'estimator':estimator,\n",
    "            'adjustment_set':adjustment_set,\n",
    "            'ignore_identifiability':True,\n",
    "#             'conditional_estimator':conditional_estimator,\n",
    "            'data_transform':data_transform,\n",
    "            },\n",
    "            boot_samples=boot_samples,\n",
    "            seed=seed,\n",
    "            )\n",
    "\n",
    "\n",
    "    # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go\n",
    "    intervention_values = np.repeat(dox_vals.reshape(len(dox_vals), 1), axis=1, repeats=len(X))\n",
    "    estimated_causal_effects = causal_effects.predict_total_effect( \n",
    "            intervention_data=intervention_values,\n",
    "            transform_interventions_and_prediction=True,\n",
    "#             conditions_data=conditions_data\n",
    "    )\n",
    "\n",
    "    # # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go\n",
    "    if confidence:\n",
    "        estimated_confidence_intervals = causal_effects.predict_bootstrap_of(\n",
    "            method='predict_total_effect',\n",
    "            method_args={'intervention_data':intervention_values,\n",
    "                        'transform_interventions_and_prediction':True,\n",
    "#                          'conditions_data':conditions_data\n",
    "                        })\n",
    "\n",
    "    if fig_ax is None:\n",
    "        fig = plt.figure(figsize=figsize)\n",
    "        ax = fig.add_subplot(111)\n",
    "    else:\n",
    "        fig, ax = fig_ax\n",
    "    \n",
    "    if confidence:\n",
    "        ax.errorbar(intervention_values.flatten(), estimated_causal_effects.flatten(), \n",
    "                 np.abs(estimated_confidence_intervals - estimated_causal_effects), \n",
    "                 alpha=1,\n",
    "                 label=label,\n",
    "                   )\n",
    "    else:\n",
    "        ax.plot(intervention_values, estimated_causal_effects, \n",
    "            alpha=1,\n",
    "            label=label,\n",
    "               )\n",
    "    # ax.set_ylim(-0.25, 0.35)\n",
    "    ax.grid(lw=0.5)    \n",
    "    ax.set_xlabel(r'Intervention value in $X$')\n",
    "    ax.set_ylabel(r'Estimated effect on $Y$')\n",
    "#     ax.legend(loc='best', fontsize=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plot overlays the ground truth, the linearly estimated causal effect, and the nonlinear estimate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f87259503d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 3))\n",
    "ax = fig.add_subplot(111)\n",
    "fig_ax = (fig, ax)\n",
    "\n",
    "X = [(1, 0)]\n",
    "Y = [(3, 0)]\n",
    "\n",
    "# Ground truth\n",
    "ax.plot(dox_vals, intervention_data[:, mask_dict[0][:,Y[0][0]]==False, Y[0][0]].mean(axis=1), \n",
    "    alpha=0.8,\n",
    "    color = 'black',\n",
    "    linestyle='solid',\n",
    "    label=\"Truth\")\n",
    "\n",
    "# Estimation using LinearRegression\n",
    "fit_predict_plot(X=X, Y=Y, graph=graph, dataframe=dataframe, \n",
    "                 estimator=LinearRegression(), \n",
    "                 dox_vals=dox_vals,\n",
    "                 data_transform=None, \n",
    "                 graph_type='stationary_dag', \n",
    "                 adjustment_set='optimal', \n",
    "                 confidence=False,\n",
    "                 seed=4,\n",
    "                 label='LinReg',\n",
    "                 fig_ax = fig_ax,\n",
    "                 verbosity=0)\n",
    "\n",
    "# Estimation using MLPRegressor\n",
    "fit_predict_plot(X=X, Y=Y, graph=graph, dataframe=dataframe, \n",
    "                 estimator=MLPRegressor(random_state=2, max_iter=1000), \n",
    "                 dox_vals=dox_vals,\n",
    "                 data_transform=None, \n",
    "                 graph_type='stationary_dag', \n",
    "                 adjustment_set='optimal', \n",
    "                 confidence=False,\n",
    "                 seed=4,\n",
    "                 label='MLP',\n",
    "                 fig_ax = fig_ax,\n",
    "                 verbosity=0)\n",
    "\n",
    "ax.legend(loc='best', fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear regression fails while the MLP better estimates the nonlinear effect.\n",
    "\n",
    "It is prudent to also estimate confidence intervals, this is implemented based on bootstrap here. The following cell can take a while:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8725977550>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 3))\n",
    "ax = fig.add_subplot(111)\n",
    "fig_ax = (fig, ax)\n",
    "\n",
    "X = [(1, 0)]\n",
    "Y = [(3, 0)]\n",
    "\n",
    "# Ground truth\n",
    "ax.plot(dox_vals, intervention_data[:, mask_dict[0][:,Y[0][0]]==False, Y[0][0]].mean(axis=1), \n",
    "    alpha=0.8,\n",
    "    color = 'black',\n",
    "    linestyle='solid',\n",
    "    label=\"Truth\")\n",
    "\n",
    "# Estimation using LinearRegression\n",
    "fit_predict_plot(X=X, Y=Y, graph=graph, dataframe=dataframe, \n",
    "                 estimator=LinearRegression(), \n",
    "                 dox_vals=dox_vals,\n",
    "                 data_transform=None, \n",
    "                 graph_type='stationary_dag', \n",
    "                 adjustment_set='optimal', \n",
    "                 confidence=True,\n",
    "                 boot_samples=50,\n",
    "                 seed=4,\n",
    "                 label='LinReg',\n",
    "                 fig_ax = fig_ax,\n",
    "                 verbosity=0)\n",
    "\n",
    "# # Estimation using MLPRegressor\n",
    "fit_predict_plot(X=X, Y=Y, graph=graph, dataframe=dataframe, \n",
    "                 estimator=MLPRegressor(random_state=2, max_iter=1000), \n",
    "                 dox_vals=dox_vals,\n",
    "                 data_transform=None, \n",
    "                 graph_type='stationary_dag', \n",
    "                 adjustment_set='optimal', \n",
    "                 confidence=True,\n",
    "                 boot_samples=50,\n",
    "                 seed=4,\n",
    "                 label='MLP',\n",
    "                 fig_ax = fig_ax,\n",
    "                 verbosity=0)\n",
    "\n",
    "# Also show observational density of Tair\n",
    "ax2 = ax.twinx()\n",
    "x = dataframe.values[0][:, X[0][0]][dataframe.mask[0][:, X[0][0]]==False]\n",
    "density = gaussian_kde(x[np.isnan(x)==False])\n",
    "ax2.fill_between(dox_vals, \n",
    "    y1=np.zeros(len(dox_vals)), y2=density(dox_vals), \n",
    "    color='grey', alpha=0.5, label=r\"$p(%s)$\" %var_names[X[0][0]])\n",
    "ax2.get_yaxis().set_ticklabels([])\n",
    "ax2.get_yaxis().set_ticks([])\n",
    "ax2.set_ylim(0, density(dox_vals).max()*4)\n",
    "ax.legend(loc='best', fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The larger (bootstrap) confidence intervals at the left and right ends illustrate that the estimation worsens in regions with little training data (density shown in grey). As a side note: the graph was estimated via causal discovery from the same data as the effect was estimated and, therefore, the confidence intervals could be too small (see comment on post-selection inference in paper). A compromise would be to split the sample (via masking)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation using alternative assumption about graph\n",
    "\n",
    "An alternative graph can be assumed where hidden confounding between Tair$_{t-1}$ and Reco$_t$ as well as between GPP$_{t}$ and Reco$_t$ is present. This hidden confounding is graphically represented by the bidirected edges Tair$_{t-1}$ $\\leftrightarrow$ Reco$_t$ and GPP$_{t}\\leftrightarrow$Reco$_t$, and it can emerge from other atmospheric and hydrological drivers. The causal effect in this modified hidden-variable graph (``graph_type='stationary_admg'``) can still be identified, but the optimal adjustment set changes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = np.copy(original_graph)\n",
    "\n",
    "# Add confounding links, '+->' indicates the existence of both a causal effect `-->` and confounding `<->`\n",
    "graph[2,3,0] = '+->' ; graph[3,2,0] = '<-+'\n",
    "graph[1,3,1] = '<->'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are interested in lagged total effect of X on Y\n",
    "X = [(1, 0)]\n",
    "Y = [(3, 0)]\n",
    "causal_effects = CausalEffects(graph, graph_type='stationary_admg',\n",
    "                    X=X, Y=Y, \n",
    "                   verbosity=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = [('Tair', 0)] -----> Y = [('Reco', 0)]\n",
      "Oset =  [('GPP', -1), ('Reco', -1), ('Rg', 0), ('Tair', -1), ('Rg', -1), ('Tair', -2)]\n",
      "Optimality = True\n",
      "Mediators = {(2, 0)}\n"
     ]
    },
    {
     "data": {
      "image/png": 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rbMWKFcjKyjKSRhSx+Oqrr1C1alVW2ffff28kbShiwTAMFi1axCqLi4vDxo0bBWuTOmoeUH3lTUhIoFG1GWBra4uZM2eyyg4fPkz7qs2Atm3bcqboLlu2DDk5OYK0Rx01D7Rq1QoffPABq2zFihV0N3IzICAgAFWqVGGV0ajaPFAN0GJjYwWLqqmj5glVo8XHxws+wEAxPnZ2dpyo+t9//0VoaKiRNKKIRfv27eHn58cqEyqqpo6aJ9q0aYPevXuzypYtW0ajajNg4sSJcHJyYpWJtbSYYlxUA7SYmBhs2rSJ93aoo+YRdVH1b7/9ZiRtKGJhZ2eHGTNmsMoOHTqE27dvG0chimh06NCBMwNk2bJlyM3N1XCFflBHzSNt27ZFr17sBPLLly8XbICBYjpMmjSJs4Sc9lWbB6oBWnR0NH7//Xde26COmmfUDTDQqLr8Y29vz4mqDxw4gDt37hhJI4pYdOzYEd27d2eVLVmyhNeomjpqnmnXrp1oAwwU02LSpEmoXLkyq4xG1eaBuqj6jz/+4E0+ddQCoC6q3r17t5G0oYhFxYoVMX36dFbZP//8g/DwcCNpRBGLTp06wdfXl1W2cuXK4hTJhkIdtQB06NAB77//PquMOmrzYPLkyZyd6P/66y8jaUMRkwULFrCOX7x4gRs3bvAimzpqgRg1ahTr+OzZs4iNjTWSNhSxqFixIgYNGsQqow9p86BLly5wd3dnlfFle+qoBaJfv36oUKFC8bFCocDevXuNqBFFLIYNG8Y6vnfvXvG2bZTyC8MwGDp0KKvs77//Zm00oC/UUQuEnZ0d+vTpwyqjkZV50K1bN87+mdT25oHqQzo2NhYXLlwwWC511AKiarSrV6/i5cuXxlGGIhpSqRSDBw9mle3evZu3gSWK6dKsWTM0a9aMVcbHQ5o6agHp3bs3HVgyU1Qf0i9evMDNmzeNpA1FTFRtv3//fuTn5xskkzpqAbGysuJs2UQdtXnQrl071KlTh1VGbW8eDBkyhHWckpKCU6dOGSSTOmqBUX263rlzB48ePTKSNhSxEHJgiWLa1KtXD23atGGVGdr9QR21wHTr1g3Ozs6sMjqwZB6oPqSjo6Nx6dIlI2lDERNV2x86dMigTJrUUb/h+fPnWL58OQ4ePMgqT0pKwvLly7F161a95MpkMjqwZOKcPXsWy5cvR0hICKs8JCQEy5cvx7lz5/SS27x5czRp0oRVRh/SpsXWrVuxfPlyJCUlscoPHjyI5cuX4/nz53rJHTx4MBiGKT7OysrC4cOH9VeUUAghhGRnZxMXFxcCQO3fypUr9ZZ95coVjrybN2/yqD3FEEJCQjTaHQAJCQnRW/aiRYtYshwdHUl+fj6P2lMM4aefftJodxcXF5KTk6O37G7durHk9evXT29ZNKJ+Q4UKFTBr1iy156pVq4Zx48bpLbt9+/Zwc3NjlekbpVH4x9vbG/369VN7rn///vD29tZbtmo/dXJyMu7evau3PAq/jB8/ntM1WcTs2bNhbW2tt2zV7o/z58/r/SZNHXUJxowZAxcXF075t99+CxsbG73lMgyDLl26sMpUX7MpxkU1kVYRqvkbyoqHhwdcXV1ZZdT2poONjQ2+/fZbTrmLiwvGjBljkOyuXbuyjtPS0vDixQu9ZFFHXQJ1UbWh0XQRPj4+rGO6U7VpoS6qNjSaLoLa3rQZP348ZyWpodE0oHxI29vbs8r0tT111CqoRtWGRtNFtGzZknUcFhaG9PR0g+VS+EM1qjY0mi5C1fY0ojYtbGxs8M033xQf8xFNA4BEIuE86PW1PXXUKpSMqvmKpgHAy8uLNQoMgO6pZ2KUjKr5iqYBbkR99+5dFBQU8CKbwg8l+6r5iKaLULU9ddQ8UhRV8xVNA8qtmho0aMAqo6/ApkdgYCAYhuEtmga4EXVeXh4ePnzIm3yK4RT1VfMVTRehavvg4GC9BhSpoy4JIUBmOCqkXMDKucMw7qOaQEIQUJDJi3i+nq4UAVAUACl34e0cjd+XB8DbORpIuassN5AaNWqgevXqrDJqexMiMxMICsL4WrWwctgwWJ8/D4SHK/2Bgaje88nJyYiMjCyzHJnBmrzrFOYCkfuBF1uApBuAPAMAMLQygOIcOgxgVxdw7QN4jAUcmmiSppWWLVti165dxcf0ZjUyufHAi21AxG4g9T5AlE55dA0AF9Yp6zAWQKVmgNswwN0fsFY/las0WrZsiWPHjhUfh4SEcDaXoIjIw4fAxo3A4cPFTtkGwFAAWLlSWcfeHmjTBhg1Chg0CNCjO6RBgwawtbVFVlZWcVlISAhnum5pmG9EXZgP3FsE/FMduPoZEH+u2ElzIUDmC+DpOuBoU+BUJyC57E5W9en6+PFjlgEpIpETBwR9DhxwBW7PAlJCi500B1KgPH97lrJ+0OfK68sInflhIgQHA506AU2bAuvWAS9eaI6cMzKAc+eAzz4DqlcHFi8GypgFTyqVwsvLS0WFstvePB114nXgWAvgXiBQkKYsI4rSryNy5b9J14ATrYHQbwG57ruLqw5OKRQK3LlzR+frKQZCiDKCPtIAiNj1xp462B1Q1iNy5XVHGirllOHVWLWv8vbt2zRBk5jk5ADffKOMkK9dU5bJ5aVfp3jz+0hLAxYsADw9gTLug8jHrB/zc9QvdwP/dQIyn0G5slMPSCEABfDof8CZbkBesk6XOTg4wMPDg1VGIyuRIArg1lfAtZFAQcYbG+ojpxAoSFfKCZ6s2wMe3Ig6JycHjx8/1k8HStlITga6dQN++knpePV9QBIChIUBHTsCZUhZq+5tqqwDiublqMN3AkHDlZGRvjcqCwWQfAs44wvkp+p0harRaEQtAoQAN8YBYb8WFRgqUPnP03XAjfE6RdY1a9ZE1apVWWXU9iKQmgr4+gK3br2Njg2hsFD59+mnQInxJm2o3vPx8fGIiytb95n5OOqUu8ooyOCbVAVSCKTdB65/odMNqzqIkJysWzROMYBnG4Dnv4N324MAzzcBz34rtSbDMNT2YkMIMHo0cP++/lG0JrmEAP7+gA55W9QNHJbV9ubhqAvzgCtDwf+N+gZSCLz6B3hZ+hPWzs6OdUwHEwUmPQwIni5sG8HTlO2UgqrtMzP5mfZJ0cDOncCBA/w66ZIQAgwbBuTlaa2mbi1GWW1vHo76yWog/TFP3R2aYIBbAaXOuaY3q8jcnPB2EFgoiFxp+1KgtheRjAwgIABQWQ3MK4WFwKNHwM8/a60mlUo5ztrojnrBggXw9vaGt7c3mjdvjkaNGhUfe3t749atW2qv+/fff/Hhhx/yrQ6gkAOPV0OwaLoYohykCv9Tay16s4pI2kMg7ow4jjr2NJCmfYs1ansR+fNP5UIWoTfoIARYvbrUGSSG2p53R/39998jNDQUoaGhWLhwIWrUqFF8HBoailatWqm9rm/fvjh69Cjf6gBRB4HcWP7lauLxKq0/Dnqz8oNOU9ue/gIwIq3pYmTK9rRAu734oVTbFzlPsYiJAQ4d0lrFUNuL2vVx9uxZDB06FO3bt4enpyc+++wzvHz5EgDwzz//wNfXFwBw/fp1dO3aFTNmzECrVq2wceNG/RuNPibezQqinPaX9VJjDeqo+WHJkiUYO3Zs8e9HLVGHhI+miyByZXtaoLbnh0GDBmHJkiXIyNCwQC08HHj2TPhougiZDCix6lQdhtpetCXksbGxmDJlCn7++Wf4+voiJSUFkyZNwrp167BixQq19d3d3bF06VLkldJZr5WEIPFu1jcUxl+Hwqqm2nOqWbkyMjJoJjU9yM3NxaZNm7BlyxaMGjUKc+bMQZ06dUpUSAByosVVKuc1CjKiAeuqak9XqFCBdZyenk5trwcJCQmYM2cO/ve//2HmzJmYNGkSO+/zzZuaLxYCuRwICtJa5Z1x1I6Ojjh69Chq166NzMxMxMbGonLlylrnE3788cewsLCAhYWFfo0W5gIZpY/G8wpjgSlzVmDd38NKrwvlwgdLS0uBlSq/yOVy9Q5bjyX+fODp0x6PwnRLunPs2DFqewNITk5W77BDQgALC0DMh+DTp0BursZ8ICbXR60JCwsLHDlyBF26dMGHH36IlStXIikpSesKHU17melMfhp0XyLMF0T5gKCISpHDrl+/vrJL5MUT4ygi6MwiijqKHHadOnWUXSJxceJ1exShUABaNgJ5Zxz18ePHsWPHDmzfvh0XLlzApk2b0KSJ9ix0qon2y47IxqIYHblcjvDwcMTEpxhbFYrIpKenIzw8HCmGdJUagpaHg+qWXCbrqDMyMiCRSGBtbQ1CCC5evIiDBw8K20dnYV96Hd5hkJsvdhRPAYDu3bvj0qVL+O+//9C+rfrZRUKTl1e27GoUw5HJZBgzZgzCwsKwceNG1K5RQ9j505qw1+xv3pk+6gEDBiA4OBgffvghpFIp3N3d4e/vj507dyK/jKkDdUZmC9jW0ToLg3dIAb6eNBx3og9pnDOuCsMwcHFxgZeXF/r374/OnTtDIjGPtUj6smbNGqxbp8wZ3aNHDwQGBqJjx45vK1T2NIpePy1fhAnTvkNsrG5TQi0tLVGrVi20a9cOQ4cO5ewCROEybNgwhISEQCaTYfTo0Zg9ezZ7INnTU9z+aQCoWxfQshuUQiXPSFkzJzJEn31h3iWuDAci/xa37/DDRyi0q4/Fixfj+++/5xipNIoeZL1798bEiRPpzauGwMBAXL16leugiyAE+McZyEsUTymrKsCgBCQnJ+OLL77AwYMHyyzC2toazZs3x8CBAzF27Fg4Ojryr+c7jq+vL+rXr8910EU8egSU0q3KK1IpMHQosGOHxir9+/fHoRJzrb/++mssX75c5ybKv6N+vgW4Plq89qyrAwNeA4wyIr5w4QKGDRuGmJgYvUXa2NjA09MTn3zyCb744gtUrFiRL23fWdLT00v/HoI+AyL+Fmd6JiMD3IYAHZQ3KyEE69atw/Tp0w3q3nNyckK7du3w+eefY9CgQZBKpXxp/M5Squ0VCsDVFdDxrYYXNm9W7gSjgdatW7PesFetWoWpU6fqLL78v1+7DQUsHERqTAI0nFLspAGga9euCAkJQefOnfWWmp2djatXr2L69OlwcHCAq6srvvrqK4Oc/7uOTg+rBl+Ju+ClweTiQ4ZhMGnSJFy8eBGurq4AuP2UupCUlISjR49iyJAhsLS0RJMmTfC///3PsLUF7zil2l4iASZPVv4rBg4OyohaC9HR7Dn9Rb8JXSn/jlpWAag/AWBEiEQkMqDel5zi6tWr48yZM5g6dSqqVKnCOtesWTMMHz4cdevW1blfOjo6Gr/88gtq1KiBWrVqYcaMGUhKSuLlI5QrqrQFKrcU3vaMFHD0Aaq04Zxq164dgoOD8d5778HBgR0wfPTRR/jggw90noaqUCjw6NEjfP3117CxsUHz5s2xdu1aumhGHWPGKFcMCo1Uqkz+pLKYqSSFhYWcMYsaNWqUqZny76gBoMkswLoaBP+4nksA6ypqT1lYWGDVqlXo3r07+xJPT+zYsQMvXrxAQUEBDh06hP79+3OSzGsiKioKK1euRJUqVVCnTh3MnTsXaWlpBn+UckPr9eIk5mm9XuPpatWq4b///oOVlRWrfMCAATh69Cji4uKQmZmJdevWoXPnzjpF3gqFAvfv38fkyZNhbW0Nb29vbNq0iW7vVUSVKsCPPwrbhkSi3Etx1iyt1eLi4jjjVDSiVoelw5u+Q4GmzTEyoGonoNHUUqvm5LD3WCxpMIlEgr59++LAgQOIj49HWloaVq5cifbt26vNaatKREQEfvzxR1SqVAmNGzfGjh07yrzlT7mjShug2TwAQk3XYoBm8wGn1lpryWQyJCQksMpKRlW2trYICAjAxYsXkZGRgfDwcMycORNNmjSBrJTIUKFQ4Pbt2xg7diysra3x3nvv4abYy6hNkalTldtmCRVZE6IcQCylK0a12wMAXFxcytSUeThqAKjWDfBayr9cRgZUqA503M3qm9aEqtG0vQJVrFgR06ZNQ1BQELKysnDv3j34+/vDycmp1HYeP36MESNGwN7eHmPHjkVKihkvAGk2D3DpBf5/7hKl3GZzS62ZkZHBSSKkLaqqU6cOVqxYgQcPHiA/Px9Hjx6Fn58fJ1+IKnK5HBcuXECbNm3g6uqKFStWlHnWUblBKlXubVi9ujDOeulS4L33Sq2mes9XqVKF83ZVGubjqAGgybfK7gkAvERYjBTXo6oit9NpwEZ9EiZVXr9+zTouyytQs2bNsHXrViQmJuLRo0cYPnw4KlWqpPWarKwsbNq0CU5OTmjbti3Onz+vc3vlBokF0OUfoMYH4CuyJgS4lNhWKVdSei4adVGVrv2UDMPggw8+wMmTJ5GdnY1jx47B19e31Js9Ojoa33zzDSpUqICBAwciIiJCp/bKFTVrApcuKf/lY8YMwyALwK1Jk5S7muuA6j1f1v5pwNwcNQA0nQV03g9YVjZgkEmCzFxg8v66aP9tLJb/+rdOV/ExqFBEo0aNsGPHDqSkpODOnTv45JNPtI6GE0Jw48YNdOvWDc7Ozvj+++/Nqz9Tag10OQA0W6B8C9LX9owML+Kl6LG2LrpMuYpLQbp1Mag6aisrK73nSPfu3RtnzpxBTk4ODhw4gM6dO2tNXJafn48DBw6gTp06aNy4Mf755x+92n1nqVMHuHED6N9feazvbBCpFGft7NCiWjV88PffOr+lGjrjAzBHRw0AtQYCfcKAup9D+RXo+DW8yWt95aUjmgVWxdr9z0AIwY8//ojnz5+Xenl8fLzBgwrqaNGiBfbs2YO0tDRcunQJXbp00TqDJCEhAYGBgbC1tcW0adPMZ9aARAa0+A7ofRtwaqcs0zVX+Zt6G67WRvO5ljhzPRwAMGHCBJ2+P3VdXobmsmEYBv3798fFixeRm5uLzZs3l7o46vHjxxg0aBCqVauGbdu2GdT+O0XVqsC+fcDBg0BR/7Cu3SESCeQMgzH16uH9jAy8iItDQkIC5s4tvcsLKFt3p0YVynxFecHKEWi3Gej/CmixULnUvBjmTcRV4uuRWCn7I7sehV3vk3gV/XY6XF5eHiZNmlTqwN2zZ884ZdWrVzfsc6jQqVMnXLhwARkZGZg2bZrWrpG8vDysXr0a9vb2mDZtGuSlbCdUbqjUFPC7DPQOBTzGABaVSpwssn0JJ2pRSVmvdygK6k9FdvbbAeEHDx7g51L2zAO4ttf3TUoTEokEo0aNwpMnTxAeHo7+/ftrjbLj4+MxcuRIODs7m5fD7tcPiIgAjh4FevUCSnYfSSTK7pGSD9A6dYCFCyGLikKiymrHDRs26DRoy4vtCeUtuUnkwj+riG+7hsS3rQfxbVefjPy4CyGpDwgplLOqTp06lUCZnq/4b//+/VrFL1q0iFW/cePGQn6aYg4dOkSaN2/O0Vf1z8rKikydOpXk5+eLopfJoFAQkhlJVn/3JfFt1+CN7RuQ1d99SUhmpPL8GwoKCoi3tzfre7O1tSWRkZFam+jWrRvrmokTJwr9qUhBQQFZsmQJqV69eqm2r1q1KtmyZYvgOpkccjkhDx6QkV26EN/69YmvhwfxbdiQXFi1ipCkJFbVly9fEhsbG9b35uPjQ+RyuXrZhJCcnBxiZWVVJj+hDuqoVdi3bx/rS23UqJHaemlpacTFxYVVt2bNmiQjI0OjbF9fX1b9gIAAoT6GWl6/fk2GDBlCLCwsdHLY2n6A5ZGAgACd7HPt2jXCMAyr7sCBAzXKVXez7tu3T6iPoZarV6+S9u3bc/RW57C3bt0qqm6mQKNGjXSyz9KlSznf2S+//KJR7tmzZ1l1GYYhiYmJZdaPOmoVdHXUhBDy119/cYw2e/ZstXVzc3OJtbU1q+7evXuF+hhayc3NJRMmTCjVYdva2pJNmzYZRUdjoKujJoSQsWPHcr6v//77T23dc+fOceomJCQI9TG0EhERQXx9fUt12G5ubuTGjRtG0dEY6Oqo8/LySJMmTVh1HRwcSJJK9F3E/PnzWXU9PT310s98+6h5YPDgwejRowerbM2aNUhM5GZsu3btGnJz2Tu/vKfDHEwhsLKywq+//oqMjAxMmDBBY19mVlYWxowZgwYNGuD+/fsia2naLFmyhJMOIDAwUO04xblz51jHLVq04FwrFrVr18aZM2cQERGB999/X+OAZkREBNq0aYPevXtr3kTWDLG0tMSvv/7KKktLS8OqVavU1le1fbdu3fRqlzpqA2AYBmvXrmXNsMjKysLKlSs5dVUN1rx5c6PdrEXo6rDDwsLQvHlzfPzxx5yVleaKo6MjfvjhB1ZZUFAQzpw5w6nL183KJ7Vq1cLp06dLddgnTpyAk5MTfhR6OfY7RNeuXTFkyBBW2Zo1azjT9bKzs3H9+nVWGXXURqJhw4b49NNPWWVr167lJEkyxZu1iJIOe+zYsRpTae7fvx+Ojo5Yu3atyBqaJiNHjoSbmxurbOHChayoOjs7G9euXWPVMSXbl3TYmt7wCgoKMHfuXNSoUQOXLl0SV0ETJTAwkPVwS09Px+rVq1l1rly5wpq6KZFI0KVLF73ao46aB+bNm8eKqjMzM1mvQjk5OSZ9sxZhZWWF3377DfHx8Rpv2tzcXEyePBnu7u548sRIG8iaCJaWlpy5tJcvX2Y9lK9evcq6WRmG0ftmFZJatWrh3LlzuHnzJufhU0RMTAy6dOmCXr16ITs7W2QNTYvGjRtzouqff/4Zqampxceqq4C9vb1LXUmsCeqoeaBhw4YYNmwYq2zNmjVITk4GoLxZS243xjAMunbtKqqOZcHR0RHnzp3DxYsXNSaPCQ8PR5MmTfD999+LrJ1p4e/vj9q1a7PKvvvuu+KoWvVNytvbG5UrVxZNv7LSqlUrvHz5EqtXr9a4RP3kyZNwdnbGqVOnRNbOtJg/fz4rqk5LS2NF1Xy+RVNHzRPz5s1jGS0jI6M4qlY1mJeXl0nfrEV07twZ0dHRWLRokdr+a4VCgcDAQDRv3lztAKo5YGlpiTlz5rDKLl26VBxNmXKXlzamTJmC1NRUDBw4UO35rKws9OzZE5999pnZJn1q0qQJBg8ezCpbvXo1UlNTkZmZyVkMQx21CdCoUSO1UXVKSso7e7MWMW/ePCQmJqJXr15qz9+/fx+urq7Yvn27yJqZBqNGjUKtWrVYZQsXLkRmZiZu3LjBKn+XbG9tbY39+/fj3r178PDwUFtn586dqFGjhtnOClIXVa9ZswaXL19mrfSVSqUG7fJEHTWPqEbV6enpWLJkyTt9sxZRsWJFHD9+HOfOnVOb/Ck/Px+ff/45/Pz8hNtV3kRRF1VfuHABa9eu5fVmNRbNmjVDWFgYli1bpnagOS4uDi1atMD8+fONoJ1xadq0KT755BNW2apVq3D48GFWWatWrWBvb69/Q3rNvi7HlGXBizqGDh3Kul51kYtMJiOpqakCaS8OeXl5pHfv3hoXS1SsWPGdXCxRlgUvquTm5pKaNWuyrq9SpQrruF27dgJqLw7h4eHEzc1No+2bNWv2Tv6+dV3woo579+5xvgfVpeZz5swxSD8aUfOM6quQ6iKXAQMGcPbOe9ewtLTEsWPHsHPnTlhaWnLOp6eno23btli/XvP2VOUNKysrTlSt2m/v7+8vpkqCUKdOHbx8+RKTJk1Se76oG+z27dviKmZEmjVrho8//phVpjor5vPPPzeoDeqoeUbdAENJxo0bJ6I2wvLpp58iOjoaLVq04JwjhCAgIACjR482gmbGYfTo0ahZU/0GEra2tpz59u8ya9euxdWrV9VON8vKyoKPjw/+/PNP8RUzEgsWLNB47r333kPDhg0Nkk8dtQCoRtVF1K9f/53sn9aGk5MT7ty5g8WLF6vNgb1lyxa0bNkSeXl5RtBOXKysrDBLw0anw4cP17qxw7tIu3btEB8fjw8//JBzTqFQwN/fH1999ZURNBOf5s2bY9CgQWrPjR8/3mD51FELQNOmTdX+eMeOHas1of+7zNy5cxESEqJ2wCQ0NBSurq54+fKl+IqJzBdffKE2yixPb1IlsbCwwJEjR/Dbb7+p/W3/8ssv6Nixo1lsTjFv3jxOmYODAwYMGGCw7PLpNUyAqlWrcsrKWzStiqenJ6KiotRO5UpKSkKDBg1w4sQJI2gmHtbW1hxHbWVlBW9vb+MoJBJjx47FtWvXYGNjwzkXFBSE2rVrq903sjzRsGFDzqyY2rVrqx3HKSvUUQsAIQSXL1/mlP/3339G0EZcKlasiCdPnqBv376ccwUFBfjggw+wadMmI2gmDpGRkZxNZPPy8nTaCeRdp3Xr1oiMjOSs1ASA2NhYeHh4lOv51vv27ePsQxoREcHLdFXqqAXgwoULCAsL45Tv3r3bCNqIj0QiwaFDh7Bo0SLOOUIIxo4dy0lgU174/fff1aY6NRfbOzk5ITw8HN27d+ecy8nJgY+PD0JCQoygmfD89ttvnLL09HScPHnSYNnUUQvAhg0b1JbfvXsXDx8+FFkb4zFv3jwcO3ZM7fLzadOmYcmSJUbQSjgKCgrw+++/qz33999/m82u7xKJBP/995/agdX8/Hy0bdsWQUFBRtBMOO7fv48rV66oPcfHQ5o6ap6Jj4/HP//8o/G8uURWRfTu3RsPHz5UO8g4Z86ccrWa7ciRI4iJiVF7LiYmBhcvXhRZI+OyZMkS7Nq1izPIKJfL0aVLF5w9e9ZImvGPumi6iEOHDiErK8sg+dRR88zmzZtZI9yqgwu7d+8udbfy8oaHhwfCwsLUJqJavHgxZsyYYQSt+Ed1gU+FChVYx+b2kAaAYcOG4dixY5z7oLCwED169MDRo0eNpBl/ZGRkcPLclJyem52dzVlSXlaoo+aRjIwM/PTTT6wyPz8/1vHz589x69YtMdUyCapVq4bnz5+rnQ2zcuVKTJgwwQha8UdQUBBnsLh3796s43379pldHhQA6NmzJ86dOweZTMYqVygU6NOnD/bt22ckzfhhzZo1SEtLKz5mGAbt27dn1TH0IU0dNY+o2y9x4cKFnETs5hhZAUDlypXx/Plz1KhRg3Nuw4YNmDx5shG04gfVLhxnZ2csXLiQVZaSkmK2OZw7d+6MK1eucMYrCCEYPHgwjhw5YiTNDCMlJQUrVqxglfXt2xejRo1ilR0/fpyzVVeZMChTSDni0aNHZPz48aRHjx6cHYbHjx9Pli1bpvX65ORk4uDgwLq2b9++hBBCvv32W1Z5jRo1iFwuF+NjmSTZ2dkaE/uU9j0LwcGDB8n48eM5u0s3adKEjB8/nhw8eFDr9WfOnOF8jtWrVxNCCGndujWr/NNPPxXjI5ksd+7cIVZWVpzvSyqVkuDgYNH1WbZsGRk/fjzn3u3RowcZP348efTokdbr582bx/kst2/fJsnJycTCwoJV/vvvv+utJ3XUbygoKCAeHh4as4Jt3rxZ6/WaDEYIIbdv3+acO3/+vBgfy2TJy8sj9evXV/td//XXX6LqEhYWRqRSqVpdpFIpCQsL03itQqEgHTp0YF1Ts2ZNkpOTQwghZOXKlaxztra2JDs7W6yPZpI8evSIVKhQgfNdW1tbk8jISFF1+eOPPzTe8/Xr1ycFBQUar42Pjyd2dnasawYPHlx8vk+fPqxz3bt311tP6qhLsG3bNrUGc3d3J/n5+Zz6d+/eJatXryZRUVEcgw0ZMqS4nkKh4KRRXLx4sZgfzSTJz88ndevW5XzfEomEXLlyRVRd/P391dp+5MiRauv/9ddf5OjRo+To0aOca3777bfieq9fv+acDwoKEutjmSxPnjxRG1lXrlyZZGRkiKZHfn4+cXd3V2v7P//8U+01S5cuJQ8ePCAzZszg/G5LRuA7duxgnbexsdH7TZo66hIUFBSojfK2bNmitv6pU6cIAE50oGowQgj58ssvWXUGDBggwicyfTIzM4mTkxPnO7e0tCTPnz8XTQ91UbVUKiXPnj1TW3/WrFlqba/uoa7qCNauXSvGRzJ5rl+/rvZNpk6dOqJ2DW7evJmjQ4MGDTRG023btiUSiYTTteHv78+q9/LlS47cBw8e6KUjHUwsgUwm4wwKubu747PPPlNbv2huZE5ODqt8xIgRaNSoEausZcuWrOPyujqrrNja2uLu3bucqWz5+fnw9vY2bACmDHh4eGDEiBGsshEjRqBevXpq62uyfWBgIGfAjNpePW3atMGePXs45S9fvkSnTp1E0+Ozzz6Du7s7q2zevHmcWSpFZGVlQaFQsKbhymQyTqrT2rVrw9HRkVWmr+2po1Zh2LBhqF+/fvHx/PnztRpMHfHx8Xj+/DmrzMfHh3UcERGBpKQkA7UtH9SoUQNXr17lzLVNT0+Hp6enaCv65s6dW6yDVCpVmw2tCE22f/jwIdLT01llqranjvotAwcO5ExpBYBr165x9iAVCgsLC5atGzRooLXtzMxMTpmNjQ0uXrzI+q0yDMOf7fWKw8s5f/75JwFA6tWrp3UwYcOGDRoHIgICAkhhYWFx3ezsbM5r3qlTp8T4OO8MR48eJQzDcL7LHj16iKbDyJEjCQAyatQorfUGDx6s1u4VKlQg27ZtY9U9efIkq45MJisebKQo+eqrr9R+n2J1E5Xsq96+fbvWuqpbrBX91apVi4SEhLDqqs746tKli176UUethqK+ak1900UsWrSIYyxXV1dy7NgxtfVbtGjBqrt06VIBtH+3Wb9+vdqbYNWqVaK0HxYWRqysrDT2TRfRrVs3jo4dO3YkT58+5dRNSEjg1H0X95QUmo8++ojzPUkkEnL//n1R2t+8ebPWvmlCCJHL5UQikXD0HDNmDElLS+PU37NnD6uevb09K4DTFeqoS5CdnU2eP39Obty4QdasWUMuXbpE7ty5Q+Li4tR+uaqboY4aNYqkpKRolF8UrRX9ffLJJwJ+mneXqVOnqr1h7969K0yDCgUhmRGEvDpIyOM15OSmMYQ8XqM8zoxQnleh5Cwea2tr8tNPP2kdAKtduzbr86xfv16Yz/IOo1AoOHPZARBHR0eSl5cnSJuFCgVJy80nEWnZ5GFcCtl24Ah5npxJ4jJzSZ6ce89HR0ezdKtZsyY5ceKERvnPnj3jfJ4nT56UWU/1na9mREZGBkJCQhAaGspZBnr27NnivBxSqRTu7u5o1aoVPDw8IJFIihPwNGjQAOvXr4evr6/Wtnx8fLB169biY9pXqZ5Vq1YhKCgIN27cKC5TKBTo0qULYmNjYWVlxU9DyaHAs41AxF9AQeqbQgn8bBkgmABQKIssKgFuQwGPsYCjcgOAItv36tUL69at4wxGqeLj44PIyMjiY2p7LgzD4MaNG6hevTqrHzg5Obl4GTofEEIQm5WH8NRsxGfnQUHenrNv3BK349+OMdjIpHBzqIA6lWxQQSYttrtEIsGUKVOwcOFCtQnHinB3d4eDgwPLt4SEhKBBgwZl0tlsBxNzcnJw6NAhrFq1ChcuXGB9kYDSmKRE8qTCwkI8e/YMu3fvxurVq/H48WOkpKTgu+++w507d0p10gB39P/58+dITU3l5fOUN86dO8fZYzA1NVVtnuMyk/oAONUBONESeP57CScNAAqAFKLYSQPK889/V9Y/1RE5McGwsbHB33//jWPHjpXqpAE680NXbG1tcerUKc6eo+fPn8fy5csNlh+dkYvjL+Jx9XUK4rLYThpQhrwlyZYX4lFSJo4/j8etmFS8eh2N1q1b49atW1i5cqVWJw0oHz582N4sHfXDhw+xdu1a3Llzh+OQtVFULyMjA3///TeGDRuGmTNnwtraWqfrvby8OCkfQ0NDy6a8mWBjY4MzZ85wbtjLly/jhx9+0E+oQg7c/Q447gUkvYnWiVy3a4vqJV0Hc6otHu39HIM/Hqh2E2N1qI7+37t3zywTNOlC+/bt8d1333HKZ82aheDgYL1k5skLce11Mq5FpyBXrnwI63bXv+VVeg4yneti36mzZdpaTdX2+nwGs3PUly9fxt69e5GTk2NwutHY2Fj8/vvvOkfFNjY2aNy4MauMRlaaadWqlVqnPH/+/LI/4ApzgYsDgPvfK50u0XPKHymEtUUhHCKWK+UV5up0mWpUlZ+fjwcPHuingxmwYMECdOjQgVVGCMH7779f5gdcVoEc5yKSEJOZZ5BOBECFSo64FZeBJ0ncKXqaUDdFr6y+x6wc9eXLl3HmzBne5BFCkJ6eji1btnDmzmpC9Ya9d+8eb/qUR2bPno0uXbqwyggh8PPzg0Kh0HCVCooCpVONPoayx1GaIEp5Fwcq5ZdCtWrV4OrqyiqjttfO2bNn4eDgwCpLS0sr067e2QWFuBiZhBx5IW+WB4AHiRl4qqOzVr3nU1NT8fr16zK1ZzaOOjw8nFcnXYRCoUBmZib279+v01NSNcWnat84hct///3H2XQgMTERAQEBugm4vxiIOQlWvzMvKICYE8B93bpiqO3LhpWVFc6dO8fpLjx27JhOG0UTQnAzRtnVwaeTLuJ+YgYSskuP0tWl9S2r7c3CUefm5uLAgQM69yeWFYVCgcjISFy7dq3UunZ2dqxjdaucKGwsLS1x9uxZjv02btxYevdB4g2loxbkVoVS7v1FQFLpu4xT25cdb29vTl5vABg8eHCpK1afpWQhKadAMMsDwM3oVBQUag8AbGxsOGVltb1ZOOrLly8jMzNT8C2wzpw5g+zsbK116M2qH15eXpxdYAgh+OCDD7RfeHMCINADuhiGUbZTCtT2+jFv3jzOdLbU1FROcv6S5MkVeJCYIbRqyCtU4EmydjtKJBLY2tqyykR31NHR0QgMDISvry+8vLzQpk0bfPHFF8U78kZFRaFhw4bw8vKCt7c3vL294eXlhZ49e+LgwYPFckaMGIFmzZoV1/H29ka7du0we/Zs5ObqNmCjjoKCAty6dUuUfQoVCkWpg4Oq03nozao7a9eu5WzlFRkZyUmGU0zidSAlRP+BQ10hhUBysLI9LVDb68/Jkyc5XSDbt2/XOIPiZVo2Z+qdEBAA4anZKCylMUNtb5Cjfvr0Kfr27Yv8/Hxs2rQJwcHBOHXqFPr27YuJEyfiwoULxXWPHDmC0NDQ4r/Jkydj9uzZuHr1anGdcePGsers378fISEhCAwM1FvH+/fvIy/PsNFeXSGE4Pr161ofCjSq0h+JRIJ///2XU/7DDz/g1atX3AuergUYkdZ0MTJle1qgttefOnXqqN0E+aOPPuLcb4QQPEsxbNfvslCgIHiVkaO1jqG2N8hRL1iwAB07dsSSJUtQr149SKVSVKpUCf369UNgYCArDWBJGIbBhx9+iIoVK+LRo0ca5bu6usLX1xcPHz7UW8fw8HDB+qbVkZmZqTUrHr1ZDaNdu3YYPHgwq0yhUKBXr17sioQoBxB1nSdtKET+ZsBSM9T2hrF8+XLOwFxsbCxmzpzJKsvIlyOvlH5jPmEAJGRpDwYNtb3e4UZsbCxCQ0NZS6JLUjSFJioqinMuJycHBw4cQGZmJtq1a6f2+sLCQjx58gSnTp1Cjx499FUTr169EqXboyT37t3jTMUqQjW/ckZGBp4+fSqGWuWGwMBAHDhwgBUIPHz4EJs3b8bo0aOVBTmvgbxEDRIEIi8RLx9dQ77UUf1plTe72NhYavsy8uuvv6J///6sslWrVmH69OnF91xKbunTJfmEAEgqpU2jOmoAqF69enHZ1atX8dVXXwFQOlpnZ2f88ccfAJQ78xb1MUmlUtStWxerVq1CkyZNiq/fuHEjtm3bBkD5+lKlShX4+flh6tSpeumYn58v+hJtiUSCVatW4fDhwzrVz8vLQ8OGDQXWyjwYO3YsPDw8lPOuU+4YRYcP+g3GozA13TBquHjxIrU9DxBC4OPjg2vXrqFOnTpIy5ODgXDzfNSRXVAIuYJAJlH/9m40R100qBMXF4e6desCUC79vHXrFgDgn3/+wS+//FJc/99//0XNmjW1yhw7dmyxo+cDYy3RFSvRPYVNYWEhunbtiu7du+P7CZ3Q3hhKEPFeuSlviYuLg4eHB0aNGoX+4yYD9lVE10GuUEAmkao9Z7Q+aldXVzRv3hx79+7VVwSFIgi1a9dGDefKpVeklCsIIahXrx4qVqpkbFU4GHUw8ccff8SlS5cwf/58hIeHgxCCzMxMHDx4EGvXroWzs7Mh4g3G2tpa1IHEIjRt3UURh6ysLLi5G6lLgVEfUVGER6FQoGHDhqissuxcLCwkmt2p0bo+AGUe5iNHjmDTpk0YP348EhISwDAMGjZsiC+//BKffPIJ4uPjDWnCIGQyGRwdHUXdm1ChUGDRokXYt2+f2vMJCQmcgcaHDx/Cw8NDDPXKDQsXLsQPP/wAmUwGQgiru2nPnj1Y+eNscBfuCs+d4GuAtfrX7j179rA2SnZ1dUV4eLhYqpUb3nvvPQQFBXHyPAPKKb4hzyJE7Z8GADtLKaQa+qcBbjes6pzw0jA49HN2dsbcuXMxd+5ctedr1qyJJ0+elCpn+/bthqqillq1aiElJUX3BD48tam6E3UR6h5cbm5uGutT1GNtbY2xY8dizpw52LVrF+bMmVN8jhCCIf6TcGl6DSAnWjylKrjCwt5F4+m4uDjWsaurK7W7Hjg7O2PJkiWYOHEi/Pz8WKkbEhIS8M/OP1HTt69o+jAAnCpYaq2jmoRJXf4PbZT7JeT169cX1Uk7OjqikpY+suhotuOoVKmS2lwAFO3Mnj0bv/32G9zc3DB79mw4OrKnxF2+fBkJVh3EXfBSs5/WKqq21zSFk6Kdffv2YdasWbC3t8dff/3F6d6cPXM6bGXiuTYCoLqt9l2HDLV9uXfUjRo14vQPCUm7du209ourGqysT1aKEqmU3Rf866+/cuqMWXFH3AUvDSZprUJtzw8lbe/m5oZ+/dgPyOzsbNy/eFo0faylErjYad88xFDbl3tHLZFI0LZtW1HasrKygqenp9Y6qq9ANKrihyFDhnC2xDp0PgwZtm2Fj6oZGVC9O+DQWGs1anth2LZtG2cA/4evp0Im0kQCj8q2kGhpq6CggNPlSSNqNbRr1w5Vq1YVfAbIBx98AEtL7X1VNKoSji1btnDKvvwtXxxH3Xp9qdWo7YWhYsWKnEx6KUmJiLp1SdB2GQD2ljLUq2yrtV5sbCxndTSNqNUgk8kwaNAgwRw1wzBo3LgxmjdvXmpdQwcVKJrp0qUL6tWrxyrbe+I20jz0T+qlEz6rAHvts3YIITSiFpA1a9ZwouoFkyeghp0VhAzP2rhU0jrbA+De8wzDsFZ064JZOGpAuRWSao4APmAYBs7Ozujbt69ODwI6oCQsK1asYB0TQuC/+CpQbwwgxC1bbwzgMa7UamlpacjJYWdYow9p/rC2tubc30lJSYi4ehYVrWSCOOtWLpXgYF36rB3Ve97Z2bnMs33MxlEDQPPmzTFo0CBIJBJeomuGYeDq6gp/f3+ddyKnr7/CMmDAAE7O6iNHjiKr6U9A/aKtuwy1/ZvrG3wFtNmg08YEqnYH6EOab9atW8e5r2d9/TU613JCJR0cqi4wb/5au1RCrYoVdLqGj+DMrBw1ADRr1gyjR4+Gk5OT1nrPnz/H3bt38fz5c845hmHAMAw6dOiAzz//HBUq6GYwPgYVKKVTck41oMwBMvPrb4BWa4F22wCLilpXEP53D9h5RfkvB0aqvL7dNsDnZ4DR7RZSff21sbFBxYoVdbqWohvOzs7o1KkTq+zly5e4ee0qutRyQgNHZV+ypsfq7SsXcPHwfty+ckFDDcDWUoqutZ10dtIAP92dDBE7B6iJUFhYiCtXriAoKAh5eXlgGKa4wz8oKAinTp0qrtuzZ0+0b98eEokECoUCtWrVQu/eveHionlxgzpevXqF2rVrc8pKS1ZFKRsKhQIVK1ZEVtbb5PHW1tbIzMxUTu3KiQNCvwEidr1JovR2nv3Ko8CMXW9lrfoMmNobACRKp+z2KdByBWBdtvQI27Ztw8iRI4uP69evT1OcCsDDhw/RtGlTVpmXlxdCQ0MBKFOg3olPQ3JOASvD3r9bfsO2ZW/3Zhw56zv0GTm2uI5MwqCBoy0aONppneGhDn9/f/z555/Fx+PGjcOGDRvK9sGImVNQUEDu3r1Ltm3bRn788UcyYsQI8sY25Ndffy3+/4QJE8jx48dJfHy83m2dOnWqWB4AIpFISEFBAY+fhlLEjBkzWN81APLjjz+yK+XEE/JgOSHHWhKy24KcmgW1tj+1uJ6yXo7+tp81axZLl65duxr2ASkaadq0Kcf24eHhrDppufnkdlwqOfE8jsz/Y7dauy/c/Be5GJlIItOyibxQobc+bdq0YemycOHCMsswe0ddEoVCQb766qtigxFCig23Y8cOg+XPmTOHZTBvb2+DZVLUk5eXR2QyGev7rlatmuYLCvNJ/w+7CWb7du3asXSZMWOGwTIp6jl9+jTHUffv319j/W3btwtm9/T0dCKVSlm6HD16tMxyaJq3EjAMU7z8OyAggPUvH5kAz507xzru1q2bwTIp6rG0tET//v1ZybHi4uIQGhoKb29v7gUSC8gZZR8m37bPyMjAzZs3WWXU9sLx/vvvw8XFBTExMcVlx48fByFE7SQCl2rVAAhzz1+6dImVMEwqlaJz585lF2TwI6OcsW/fPs7TeNWqVQbLzcjI4ER4hw8fNlxhikZevXrFsWWvXr001g8ICBDE9seOHWPJlEqlJC0tzWC5FM0sW7aMY8utW7dqrF+1alVWXX9/f170mDlzJktu27Zt9ZJjdrM+ykqtWrX03gqsJFeuXIFc/jbvhEQi0e/JStGZmjVrchbAnDlzRuckXR999BEvtj9//jzr2MfHh874EJhp06Zx8sEsX75cY33VWWB9+vThRQ9V2+v7JkUddSnY2mpfHqorqt0ePj4+cDBSgnNzYsqUKazjgoIC/PbbbzpdqzpDR19ol5f4WFhYcKbqPXz4EMnJyaLpkJaWhpCQEFYZddQmDr1ZjcPEiRM5q8BWr14tWvtpaWkIDg5mlVHbi8PixYs5ZfPnzxet/YsXL7Le3iwsLNCxY0e9ZFFHLQLp6en0ZjUSEokE7733Hqvs6dOniI2NFaX9S5cusW5WmUym981KKRudOnVC5crsvTN3794tWvuqwVmbNm30fkOnjloEVEd+ZTIZ57WMIhw//PADp2zevHmitK3uZhUzP7q5M2zYMNZxSkoKLl0SNqteEXy+RVNHLQKqAwqtW7emN6uItG7dGlWqsPcx1LSnJd/wNZhE0Y9FixZxyhYsWCB4u8nJybhz5w6rjDpqE4f2TxufESNGsI7T0tI4c5v5JiUlpXjpchHU9uLi6OiIJk2asMqCgoI4+aH55uLFi6w2LC0t0b59e73lUUctMDExMfRmNQHURVGqKVH55uTJk5ybtUOHDoK2SeEyefJk1nF+fj6OHDkiaJvHjh1jHbdv317n5G3qoI5aYDZv3swaTLKxsaE3qxGoVKkS6tatyyo7fVrYffU2bdrEOu7UqZNBNytFP7788kvOnOq1a9cK1l5GRgZn0LJHjx4GyaSOWkAKCws5N+vQoUPpruNGYtCgQazjlJQUhIeHC9JWWFgYzp49yyobPXq0IG1RtCOVStG4MXs/y6CgIMHa2717NzIzM4uPJRIJ/P39DZJJHbWAnDp1ChEREayy8ePHG0kbyowZMzhl2larGcLGjRtZx05OTpwHBUU8hg8fzjrOysrCrVu3eG+HEMJJYfrRRx8ZnMqYOmoBUTWYt7c3WrVqZSRtKNWrV+ck2jl8+DDv7eTm5nI22h05cqTOuwBR+Oerr77ilP3vf//jvZ1bt25xxqTGjSt9q7bSoI5aIKKiojgDFuPGjRN8J3SKdnr37s06fv36NVJTU3lt459//kFSUhKrbOzYsby2QSkbtra2cHNzY5UJMUahmp6gdu3a6Nmzp8FyqaMWiD/++IM1iGhnZ4dPP/3UiBpRAODrr7/mlPG9pFz1ZvX19UWDBg14bYNSdlS7npKSkhAZGcmb/LS0NM4g4tixYzkDmfpAHbUAyOVy/P7776yy4cOHw97e3kgaUYpo2rQpJ3PdX3/9xZv8R48e4eLFi6wyPl59KYYzc+ZMThmfUzR37tyJ7Ozs4mOZTMbbADJ11AJw7NgxREVFscrozWo6dOnShXUcFhbGWuJvCKrRtLOzM/r378+LbIphuLi4cHao52uMQt0gYr9+/cq8r6omqKMWANWbtU2bNup3FaEYBdWBJYVCwcuS8pycHGzbto1VNnr0aFhaWhosm8IPqvOZIyMjWXni9eXatWu4d4+9bT2fwRl11DwTERGB48ePs8poNG1a+Pn5QSZj70K3Y8cOg+Xu3buXMzA5ZswYg+VS+GPixImsY0II9uzZY7Bc1eDM3d0d77//vsFyi6COmmc2bNjAWjbs4OCAIUOGGFEjijrq16/POr527ZpB8gghWLduHausZ8+ecHd3N0guhV86dOjAyU++c+dOg2QmJCTg77//ZpWNGzcOEgl/7pU6ah5JSEjgLE0dMWIEb7vEUPijV69erOPExESDXoGPHz+OGzdusMrom5RpovqQVt2FpawsW7YMubm5xccWFhYYOXKkQTJVoY6aR5YtW4asrKziY4lEgkmTJhlRI4om1K0QTUhI0EsWIYST39rd3R0fffSRXvIowqI6uJuWlqZ3Nr3o6GjOm9Tw4cN52cGcBS9b7ZYD7t+/T/z8/EiLFi1Yuwbb2NgQPz8/8vXXX2u9/vXr18Ta2pp17ciRI0XSnqIPnp6eLHsxDMM6rlmzJvHz8yM7d+7UKmf//v2cHa///PNPkT4FpawkJCRw7KX616JFC+Ln50fu37+vVdbEiRNZ18lkMvLixQvedaaO+g0KhYK0bNlSo+EOHDig9fqAgABRDEYxnKCgIPLFF18QJyenUm9Ya2trEhMTo1GWXC4nTZo0YV3TqFEjIpfLRfxEFF3Zvn07GThwIJFKpaXa3sfHhygUCo2ywsPDiYWFBeua8ePHC6I3ddQl+Pfff9UazMvLS63Bbty4QaZOnUqCg4NFMxjFcGJjY4mbm1upNyoAMm3aNLUy1q9fT3755ReyZcsWzjV79uwR+RNRdOXChQuce1XT3+HDh9XKmDJlCjl58iQZPXo0q76VlRV59eqVIHpTR10CTVG1pmj65MmTag0spMEo/HDv3j1ib2+vdzT97bffqr3G09OTFBYWivxpKGVh69atBkXTbdq0UXvN1KlTBdOZDiaWgGEYfPfdd6wyLy8v9OvXT239kjlnSzJhwgSD0xpShKVZs2bYs2eP1ilUEyZMQPXq1dWe02T7RYsW8Toti8I//v7+mDNnjtY63333ncYEaupsb2Njg1mzZvGinzroL0qFjz76CD4+PsXHgYGBGg1WcoZHSS5dusTJ90AxPXr16oWffvqp+JhhmOJUpNbW1vjmm280XqvJ9rt27RJsMwIKfyxatIgVgHl4eBT/38fHBx9++KHGa9XZXqFQYN26dcjIyOBX0TdQR61CyahaWzQNaI6q7t69i8uXL7Oy51FMkylTphRnNfTx8cGECRMAaI+mAc22P3/+PB4+fMi/ohRekUgk+PPPP9GwYUMAwIABA+Dl5QVAezQNQK0zzs3NxYkTJxAdHS2IvrSPWg0KhYL4+PiUOtMjMDBQbd/WvXv3xFGUwgtZWVnE09OTfPPNNyQmJoY4OjpqnelBCCGdO3fm2H748OEkKSlJJK0pfPDw4UNiZ2dHTpw4QQ4cOFDqTI+8vDyO3S0tLcmSJUtIQUGBYHoyhAi8b/o7glwuR3x8PGJjY5GYmIinT5/C3d0dtra2cHFxgYuLC+zs7FhP2vHjxxev8be0tMT8+fPx7bffcpaoUkyc/DTEPDyOvNirqFNVituPX8OrkStgWwuo3BKo7AVYOrAuadCgAcLCwgAA1apVw4YNG2iWvHcMQoDoaGD9+mtgGG+kpVkiPv4uatXyRMOGgLc30KwZYGX19pqoqCjUqlWr+LhVq1bYunUrmjZtKqiustKrlF8IIXj9+jWCg4Nx79694lSXRYNBwcHBIMqZMQCAKlWqoHXr1mjRogWsra0RExMDAHjvvfewYcOG4tcoyjuAogB4fRh4uh6IOwMXEAASIEUKLwmApwBIIQAFAAao/j5QfwLg2geQWCAmJgYMw2D8+PH48ccfUalSJWN+GkoZSE0Ftm8Hfv0VePwYANpBKgWUt70nAKCgQFnXygoYNgwYNw5o2xbF97y9vT1++OEHBAQE8LIxQGmYbUSdlJSEf//9F5GRkZBIJGXqT5bJZPD19cXixYsxZMgQjBgxgm6x9S4RfRK48SWQHQUw0jcOuRSK6tnURGbTtej4cSA2btyItm3bCq8vhRcKC4E1a4C5c4Gi1By6eD+ZDJDLgc6dgeHDD+P06T+xevVquLq6CqtwCczOURNCcPXqVZw9exaEEIMG/JycnDBs2DA4OTnxqCFFMAoygZsBwMvtUI6j62N7CQrkCqDOcFi03wBY2PGsJEUIwsKUkXFwsP4yZDKAYXKwZEkFTJ8OiBmbmZWjJoTg6NGjCDbEWiVgGAZWVlYYMWIEatSowYtMikDkJQNnewCpd3SLoEuDkQKVPAHf/wArR8PlUQQjOBjo0QNIT1dG1Xwwdiywfn1Rd4nwmJWjPnbsGG7evMmrTIZhYGlpidGjR/OfMYvCDwWZwOnOQOo9fpx0EYwUqNQC6HEJkNFUtqbIgwdAhw5AVhZ/TrqIgABAJXGeYJjNPOoHDx7w7qQBZZSen5+PvXv38rKlD0UAQmcCKXf5ddKAUl7KHSCEu2kqxfjk5QEffyyMkwaUg5F79/IvVx1m4agzMjJ428RSHYQQJCYm4ty5c4K1QdGT6OPAs9+gX3+0LiiAZxuA6BMCyafoy/z5wNOnwjhpQNlHPWYM8GYiiKCYhaO+ePEi8vPzBW/n6tWrSEtLE7wdio4QAtyaDOF/5hIgeLJuUwgoohAZCfz0EyDk4mBCgMxMYPFi4doooky/YF9fXzRv3hze3t7w9vaGl5cXWrZsieHDh5vsstnc3Fzcvn1b7x0cysqtW7dEaYeiA7GngcxnEC6aLkIBZIQBcWcEboeiK+vXizMro7AQ2LwZEDo+K3OosXDhQoSGhiI0NBS3b9/GqVOnYG9vj0mTJplkbos7d+6I1ndMCMHNmzdN8nsob0RERJRu16e/KAf8xICRAU9+EactM+fFixdazxcUKB21UF0equTlKRfQCInB74RVqlTBkCFD8Pr1a6SmpiIxMREzZ85Ex44d0alTJyxYsICVwObKlSv4+OOP4e3tDV9fX+zYsaP43OnTpzFw4EC0bNkSPXv2xNatWw12ehEREaIuRsnLy0N8fLxo7ZkrmzdvRtOmTbFjxw71DpsQIP48/wOImiByIP4c7f4QAX9/f/To0QOXL19We/7BA+Ej3JJIJMD588K2YfAS8piYGOzYsQPNmzdHpUqVMHToUNSpUwcnT55EQUEBZs+ejQULFmDlypUIDw/H+PHjERgYiP79++Px48f4/PPP4ebmBgsLC0ydOhXLly+Hn58fnjx5goCAAAAwaEffV69eidbtUcTly5dhb28vapvmxrNnz/D06VOMGDECixYtwvz58zF06FDIZG9+0pkvgIJ0cZUqSMfl//YiiziUXpeiNykpKXjw4AFOnz6N7t27IzAwEJ06dSo+L8DkLq0UFgJXrwrbRpnmUfv6+iIpKQkWFhaQy+UoKChA9erV0aNHD4wbNw6vXr3CsGHDcOPGDdjaKueVhoeHo1evXrh27Rp27tyJy5cv46+//iqW+fjxYzg7O2Pp0qXIy8vDzz//XHxu586d2L59O06c0G9EPS8vD0uXLtXrWn2RSCS4efOmoLNMKOpp0KDBW4cdexS42F90HZp8VxOPwqJEb9fcKemwp0xRdn0U5esQi/R0QKj4rMxdH4GBgbh16xZu3LiBadOmIS0tDV27dkXlypURFRWFwsJCdO3aFa1atUKrVq3wySefwNLSEq9evUJ8fDxnBV+jRo3g6OiIpKQkVlYqAKhZsyZev36t94cTY6aHOgrF6hyjsCiKsJs2bYojJy4ZRwlCxyeMwenTp9G5c2d0794dr18/MkoPlIa9JHhB764PS0tLfPnll0hLS0NAQAB2796N6tWrw9raGtevXy/OKJWfn49Xr17Bzc0NLi4uuHDhAkvO/v374eTkBFdXV0RGRrLOvXr1ClWrVtVXRaNtiUQTNBkPJycnjBo1Cl3bVQPuGkMDantj0aRJE4wZMwanTzcwSvtCJtEzuI966tSpuHnzJqZPn449e/bAzc0NS5cuxdSpUyGVSrF06VKcOXMGp0+fxocffoj169fj4MGD6NOnDx49eoSlS5di1apVGDRoEIYPH47jx48X91Fv2rQJgwcP1ls3a2vrMmfG44NKlSrRPRMFJi0tjbXThpOTE2bOnImJEycqxwdizxpFr+rVqiIjlzprIYmLi0NBiX6NJk2aYMGCBfj4448hlUpx7574OslkgIOAQxNl7qOeNGkSBg4cyCp/9eoV+vfvj759+2LcuHFYtmwZbty4gby8PLRo0QJz5swp3pPs2rVr+Omnn/DixQs4OTlh3LhxGDRoEADgzJkzWLduHcLDw1G5cmUMHjwYY8aMMSjf64YNGxAXF6f39fowZMgQNGrUSNQ2zY3AwEB8//33XAddRH4qsK+y+Ip9nAJYVhK/XTOic+fOuHz5MsdBF3HgAKDiogTH0xO4fVs4+eU+KdORI0cQGhoqalQ9bdo0VKxYUbT2zJE1a9YgOzub66BLcqgukPVSPKVs6wL9tM/xpRjOuHHj4Ovry3HQRURFASrDXYIikwFffqkcwBSsDeFEmwaNGzfmLa1paTAMg2rVqlEnLQKTJ08uvZLbEODR/8SZS81IATf9u+koulO0/Z0mXF2Bli2VEa4Y8ZlcDgwYIGwb5T7Xh7u7OxwdxckXTAhB+/btRWmLogP1A8SbhUEUyvYoRodhgGnTxHHSDAN4eCjzXQtJuXfUDMOgQ4cOorRjZ2eHJk2aCN4WRUdsawO1PxF+GTkjVbZjW1vYdig688knQPXqwif2JwT45hvh84qUe0cNAC1btkTt2rUFnTZHCEH//v3froyjmAY+PwMyewg3bY4BZBUBnzUCyafog5UVsG2bsFG1TAZ06QJ88YVwbRRhFo6aYRgMGDAAMplMEGfNMAxat26NevXq8S6bYiAVqgPt/gAg1Jg5Adr9DlSoJpB8ir74+Sl3YREiqpZIlA+D7dvF2Y7LLBw1oJzbPGzYMEgkEl6dNcMwcHd3R8+ePXmTSeGZWgMBzx+Eke35o1I+xSRZuVLpsPl0phKJMpo+cgSoLVJvV7mfnqdKREQEdu3aBblczsuUvcaNG2PgwIG0y+Nd4PEqIGQ6ACkAA2aCMFLlTJKWK4FG0/jSjiIQeXnA8OHA/v2Gy5LJAGtr4OhRZbeHWJidowaA1NRUHD58uNS8tpqQSCSQSqXw8/ODj48PXTL+LhF3Abg2CsiKgH4bCkgAWzeg/VbAWcQ7lWIQCgWwaRMwfTqQn6+cUlcWGEY5cOjnB2zcCLi5CaOnxvbN0VEDysG/e/fu4dKlS0hMTNR5qblUKkWzZs3g6+tL50u/q8hzgEfLgSdrgPzktxGyJorOWzoCDScDjb8BZBXE05fCG1FRwNy5wF9/vc2up80DymRKp96oETBvHvDpp+LsHKOK2TrqIgghiIqKwp07d/Dq1SskJiZyHHaFChVQo0YN1K9fHy1atECFCvQmLRcU5gOv/wUi9wFJ19WvYrStAzi1VU6/c+0DSC3F1pIiAMnJyoHA48eBW7eApCT2eZlM6Zw7dgT8/YF27YzjoIswe0etSmFhIVJTU1FQUACJRAIbGxvY2dkZWy2KGBSkA9lRgCIfkFgCNjUBC/rWZA7ExgKJicro2cYGqFMHsDShZzJ11BQKhWLimM30PAqFQnlXoY6aQqFQTBzqqCkUCsXEoY6aQqFQTBzqqCkUCsXEoY6aQqFQTBzqqCkUCsXEoY6aQqFQTBzqqCkUCsXEoY6aQqFQTBzqqCkUCsXEoY6aQqFQTBzqqCkUCsXEoY6aQqFQTBzqqCkUCsXEoY6aQqFQTBzqqCkUCsXEoY6aQqFQTJz/A7ArOouCJX++AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt = causal_effects.get_optimal_set()\n",
    "if opt is False: print(\"NOT IDENTIFIABLE!\")\n",
    "print(\"X = %s -----> Y = %s\" % (str([(var_names[var[0]], var[1]) for var in X]), str([(var_names[var[0]], var[1]) for var in Y])))\n",
    "print(\"Oset = \", [(var_names[v[0]], v[1]) for v in opt])\n",
    "print(\"Optimality = %s\" %str(causal_effects.check_optimality()))\n",
    "print(\"Mediators = %s\" %str(causal_effects.M))\n",
    "\n",
    "special_nodes = {}\n",
    "for node in causal_effects.X:\n",
    "    special_nodes[node] = 'red'\n",
    "for node in causal_effects.Y:\n",
    "    special_nodes[node] = 'blue'\n",
    "for node in opt:\n",
    "    special_nodes[node] = 'orange'\n",
    "for node in causal_effects.M:\n",
    "    special_nodes[node] = 'lightblue'\n",
    "\n",
    "link_width = None\n",
    "\n",
    "fig, ax = tp.plot_time_series_graph(\n",
    "    # graph = pcmcires['graph'],\n",
    "    # val_matrix=pcmcires['val_matrix'],\n",
    "    graph = causal_effects.stationary_graph,\n",
    "    # graph = causal_effects.graph,\n",
    "    link_width=link_width,\n",
    "#     link_attribute=graph_attr,\n",
    "    cmap_edges = 'RdBu_r',\n",
    "    arrow_linewidth=3,\n",
    "    curved_radius=0.4,\n",
    "    node_size=0.15,\n",
    "    special_nodes=special_nodes,\n",
    "    var_names=var_names,\n",
    "    link_colorbar_label='Cross-strength',\n",
    "    figsize=(4, 3),\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still the causal effect estimates are similar for the MLP (not for linear regression):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8725938a90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2/DEk3sLj319NT/PwRmXQmrqa7v5r+kmX/Vku+5LE7mIS7t7h+vXrFh8UpUuXfuh6y5cvz5w5cxgyZAgtW7bk2WefpXfv3rz//vu0aNGCxx9/nFOnTjF48GCGDRvGgAED6NmzJ4sWLaJWrVqUKFGC2bNnYzAY8t6ZPbjIyvHf/N/nJJ3aSdWkE4QEJeLjoUBxyzKnar1JQMPnqFi3GaU973tLOslskyqVioCAAAICAqhXz5TkTH8DU2Lfsnkz/0T9yz///GP+uXXrFhcvXuTalQu8m35k37lzZ+o+3pSmTU0/devWxfP+NjsTlcp0MjadsUqrHD8k0rJ8S0gZdR6Ntwck3YLkeEi+ZRrtk3QL3d0YvH5fWqBhO/ErKrKz4L13WHDfSnffffedXeoODQ2lX79+zJw5kwYNGtCpUyeSk5N55513iImJISAggEGDBtG/f38AxowZw8yZM+ncuTP+/v6Eh4ejVqtzHHlTFERHR/P9trUMTL//3PlJeGowd9TeUgJIqBRKqZZ9KLn1JQAa9HrHaT+YrFW+fHnKV6nBU089Zd4WFxfH2bNniTxzAq5OBCA5JYXDhw9z+PBhADQaDY0aNaJ54/q87IjAC0rGB4JPcQiqbvGQLvGuJHaRafMPpmmNs1tD9a233mLY8Dc5HXOXdp268Oarlm+TtWvXmm93796D2s2fznYfU6dOZerUqebpBO5fWzWr06dPM2nSJGbPng2YroqLiIggKCgo2/LuKjU1lf379/PbjrXUT/6FXo8kmN9ZnmqI934EY53nKNVmEKXLPkpplcppjsQLUqlSpWjVqhWtQhrCB6bEvmbNGv534gxHjx7l6NGjJCQkcPjwYY7+cYSXnzE9b+KUd2n95FO0bt3agdG7NknsIkd5Lci9atUq8zBHlUpFREQE1atXp1q1ag6ItnApisLpv/7i22++4c7/ttCtwnXeK5vNPB7/OUxQuXqFH6CTqlmjBjUffZyXXnoJo9HIhQsXOHr0KMeP/g78A8BPP/3M9/sPolKpqFKlCs899xxt27alRo0auVcuzCSxPwRfLw/OTHsajV8hzHvihKZPn86MGTMIDQ3FYDDQtGlTli9f7uiwCsWQQX2prTtDv+p3qNLQNGZQQUVatTB8Ww2FL3ubCgZWc1yQTk6tVlOrVi1q1arF812ehXmfAdC/f39+Pvwb//zzD+fOnePy5cssWbKEChUq8GTrFox3cNyuQBK7sFm5cuVYsmSJo8MoFDdu3GD96uVkrBywsMYhNJ6mEU16T388Ql5B1ex1fIOqF4lulvzI70iWAQP688awN7lx4wabN28mMjKSI0eOcO3aNb755lvGp3fZ/PfDuTzTqTPNmjVz7hOwDiCvhhC5uHHjBmvWrOHn7zbxeo3rUNm0XeOpYAyqhbrlf/B8PNx0ubuwq7Jly/LMM88wfPhwUlNT+eOPP/jp4AHQm7psvt+3j53f7aZEiRK0b9+ejh070qRJE9Rqp5vbsNBJYhcPLa++eFd0/fp11qxZww87v6Jv1RtsevIOvh5ZrjnosxF17U6m0Q+iwPn6+vLkk0/StPHjMG8+AN26vcC+H34kPj6er776iq+++opSpUrRoUMHnmrbmhAHx+xI8tEmRBbXr1/ngw8+oHeP5/E9toItT57nlZq3TUn9kWaZBau3LfJJ3dFT3g4fPpxdu3axZMkSunXrRokSJYiLi2Pjxo2MHDnKXC46OibnStyUHLE/BJUuGc1/q5ruOPFFMi7FwRce9e3zEm3L3GXTk3GU90tPVmUehfbToVobmF0p1+eLwuXh4UGzZs1o1qwZEyZM4MiRI+zdu5fDPx8ylxk0aBD1GjTk+eef5+mnn7ZtmgMXI4ldFAqDUeHs9QRu3E6hfCU9hbGAkjX0ej2bvvySvun3V7a4SHDx9JmxSlSCdpOh4Uum+UvkpKhT8/Lyok2bNrRp04bb8bcgwjQ8Uq1Wc+zYMY4dO8aHH35Ix44deeGFF+yyQpuzsjqxx8fHF7kLT4R7O3nyJB988AHG66fp+5RpW3BxnWmq1ifHQLPBlvN/FxE2zcfiZHy8M69+Xr9+Hd/vP8i3337L1atX+frrr/n666+pXr06XTo/Y75K2J1Y3cf+0ksvceXKlYKMRYhCkZCQwAcffMDg117lCcMffPHk1cwHWw6HkX9C6xFFMqm7o9KlS/Pqq6+ybds2li9fznPPPYePjw8XL15k+fIV5nInT550m2UhrU7sLVq0IDw8nBMnThRkPCIXvZ5uTq+nm3PxYtQDj61evZrHHq3Lps9N/6iLFy8yz+mSnWlv/4eXnmlN06ZNzOuetmjRgkmTJpmnE3CEgjwhpygKhw4domfPnhzbu5HlLf9lTL1b+HpkTstKu8mm+bWF21GpVDRp0oQZM2awd+9eJk+ezKOP1jU/PmbMWMLDw9myZQvJydlcRexCrE7s7733HoMGDWLQoEF8/72Vy2C5EkUBbRIqXTIqXbKpPzWXH3O5DNoHn5NnXTYcHRQvGcC2bdse2P7VV19RrFj+xlL36DuIo0ePcfz4cY4fP87WrVs5duwY06Zlv+CAK7t69SrDhw9n0ccLeCEokg1tr9AwKBW8i0On/zo6PFHI/P396d69OxEREeZtPj4+REVFMWfOHDp16sSHH37IpUuXHBfkQ8jXydOhQ4dSoUIFxowZw9ixY3M9InQpigKrnsHj3yM0sKK4BzxY7r51JrMtc7/KLWDQLmujBODJp59h+7ffMubtt80XYpw8eRKtVsujjz6ar7ruV6lSJcLCwvj5558fqh5nNGDgQCp4JbLh6VjqljQtXEGtjtDlI9MR+u4Jjg2wkLhD/3lB2bBxI/sPHGTz5s1cuXKFTZs2sWnTJlo0C2FxWUdHlz/5Hsf+/PPPExERwZw5c+jXrx8ffvgh3333nct+smVyjTHJTZq3RqfTmac9BdiyZQs9e/Z8qHoNBgNnzpxh7969LjGrnjVdNteuZS6NNqjqNb548qopqfsFmpZo67sJSj5S4LEK11C8mD99+vRhy5YtfPLJJ4SGhqJWqzl+/Li5zObNm0lMTHRglNbJ1xH73bt3Wb9+PevXrycwMJBy5crx008/8fnnn2MwGPD39+fo0aMFFWvBUang1d0Y0pI4k76c3WN5LI135loCKl0y9b9sato4NtJifcOMMrnW5aWBfPbGeHp60qVLV7Zt20abNm1ITU1lz5497Nixg4MHD+arrq83fMHubf8HmPqfS5cuTceOHRk1apTLn0Q6ePAgc96byq70lf4G1LwDQGKVpynWexkUc7FDsDyk6DLPE8iR+MNRq9U0b96c5s2bc+3aNTZv2gi3pwOwfPkKPlv9Bd27d6dPnz6WK085EasT+6xZs9iyZQsBAQEMGzaMXr164e1t+u9JS0vjzJkznDlzpsACLXAqlWkJMK/0oz9vf8jp0nijklkug7fG8mKarGVyq8uGBNq9Rw/6vBROYmIi33//PU2aNKFMmTL5rqdbnwHMmDQ22w8dR55AfRh6vZ7Fixezbt1aXg6+k/mAfxl4bgHXvR+jppsldVFwKlSowOuvvQrzpgNQtWpVzkVGsX79ejZs2ED79u3p168fjz32mGMDvY/Vif3HH3/knXfeoVu3bg+skOPj42MeWSEKXp06dQgODmbXrl1s376dgQNdYCSuYqCO+ireqpugqwzYf9Ks2NhYJk2aRNSZ43zYNJawCpkXFCUP+gFNmaoQmf1CxEJYY/nyTzn519+sW7eO33//nb1797J3716aNGlCeK8XyX75msJndWLfvXs3Hh4eeRe0QXx8POHh4bz//vs0b94cgPXr1/P5559z8+ZNypQpw4ABA+jXr1+B7N8V9ejRgzVr1pCQkEBoaGi2ZbRaLdevX7fY5uvrS/ES2a/Z6MoOHz7M1KlTqUgsX4bGUtFPi6L2QmVMv4rUL8Ch8dlKTnY6F7VabVoVqlUrzp07x7p169izZw/Hjh3j9KkTPJ0+pbBWq0OTe1UFG6e1BQsqqR89epTw8HCLi5/279/PwoULWbBgAcePH2fevHl8+OGH/PbbbwUSgyvq0qULly9f5vnnn89xLuo///yT0NBQi58ZM2YUcqQF77NVqxkx4i06BV5mdZurVPTTQkAV0vp85ejQhBurXbs27733Htu3b2fAgAH4+2d2xQ4YOID169c7bDy8Q+eK2bZtGxEREYwbN47Ro0ebt4eFhbF//36KFSuGXq/n9u3bqFQqSpQo4cBoHS9jzVOAwMBA/vrrL4vHP/9irXn63DfffIuRI0ZkW4/BqDBjQcEupluYdmxex4dNr2V2vdTtAi98gtHgGiOdhGsrW7YsI0aMoG+fcPikNgBxt+L46KOP+Oyzz+jTpw/h4eGFmr8cOm1vmzZt2LdvH507d37gsWLFihEVFcXjjz/OkCFD6NOnj9OdoFC8NCRPiIXpd2Vmx0J29p+z5ttrWl02JXW1l+lio/B1Tt/14ugpb4X9ZV0i8+0xY6hcuTIJCQl8+umndOnShYiICG7dulUosTj0iD2vkRyVK1fmxIkT/PPPPwwbNoygoCCGDBmSr31ERUWh0Vj2dhkMBnQ6HWlpaRbbjVkGqKSlpeU2KCbzttGY7QgSW+p62HJ2rctotHh9HrY+xWg0Xynwb3Q0cbfvZltXakoSGXPuRV28hK/fgx+Yx44dY+FH8/glvT+zgp8WXbFKXG8zm7TS9eDChRzrSkpKIvK+E6hZy/11NjLbfQKo9ClkLKd8ISoKxTP7uWSsKWdNO/NTLiU580Txw9Zn79geZp/3/72cKbbcytWpU4f//ve//PLLL2zdupXLly+zYsUK1qxZQ4enn+K//nnXlx1ru3ZsSuyxsbEsXbqUS5cuYTQaLR774osvbKkyWxmjbxo0aMCAAQPYvn17vhN7cHDwA5fap6amcvHiRXx8fPDNMn+swagApiTt4+OT6zj2jHJqtdqijoep62HL2bOu1NRUvLx97BebQU/6aUwqV6pEYKnsP9STEzMTfnD1amiKWZ7o/e6771j80VzebZB5Ulhf61m8eiyj8n1H6dnVFRkZSc2aNfMsl60s0/bWCA7O8Vta1voqPFIl2/qs3ae15f5KSbKqnDX12Tu2h9nn/X8vZ4rNmnK1a9dm0KBB/PLLL6xatYqTJ0/y88+/wDN515cday+OsimxT5o0iVu3btGuXbsHhj7aw5o1a/jzzz/5+OOPzdu0Wi0lS7rfaA5hvbVr1/Llp/NZ2jyGegGZ3ya0zy/DsxC6XmSEirCFSqWiTZs2tG7dmuPHj7Nm1UrgnwLdp02J/dSpU+zZs6fA5mcPCQlh3rx5fPfdd3Tq1Injx4/zxRdf2H1yKle/utLVmF/ufJ7TNBqNLFy4kBM7V7K2zTVK+xpQfANQpd5Jr09OkgrnlzG7ZN3as2He+gLdl00nT4sXL26+6rQg1K9fn4iICJYtW0ZISAjTp09n8uTJ2Z5ktUXGtwxXn5rT1STrAIM2x+GZ2dHpdEybNo07B5eyvGU0pX0NUPYxUvttL7hAhXBxNh2xDxs2jEmTJjF48GBKly5t8VjFihVtCuTs2bMW98PCwggLC7Oprrx4eHgQEBDAjRs3ANBoNKhUKgxGBUVvmvkvNTU11z7ljHJpaapsTxbaUtfDlrNnXWlpadbXZ9BT22iaIz41pTYeHpb/VoqikJh4j1vxdwi4vAuPRm2zred+ySmpvDdxEs3u7qRfozumjXW7QPdlKDpjrs8VoiizKbFPmTIFgH379gGmrxiKoqBSqfj777/tF10Bypi8JyO5AxgVhRt3TCcBPZN9UefwFT9rORI88c7mPIMtdT1sOXvWpdPp8PD0zLFc1l4sRTGiSjAN41Lu+aDKLjajkZLnt1Hy3BZSdO9D+hC/jHoyqktO02NQ/FCAyRPHMMjvAC1rmE4YpbYcQ0rr8aBXk5ycQIpS3PScJC3JpGWpy3QjJVGLnxKAAqTcS8PXkMrNJB3+d1NQlCz7TEzFTymNokDqnVR8tV73xaagKJCSnIKv0fR/kxKXgm+KB4rFa6Gkx5OMr9G06HXKjSR8ElWW9SmQkpyIr7EqCpB6PRFfPx7YJ2SUC0ZBRUr0PXz9jNnu88KNNFKMdUzPuXIXXz+Duasxo6iiQFpKIt4G07Dh1It38PHTmR/L2GdqchI+hgYoqEi7cBtv3zSLekgvm5aSjLfBNI1I6rk4fHxT02Oz3G9aShLehidM5f6+hY9vskVMAKmpSfgYmpv2efom3r5JKIpCbGwCpxOjs5RLxsfQGgUV2pM38PK5l6WuzP1qU1Pw1puuyE47fh1v37sWf/OM9mpTU/DSmyYC0B69hpfPHXNQ5vhTU/DWd0QB0n6Pwdsn3uL/NmO/2rRUvPSmXoW0X6/i7XPrgf9vRQGdNhWNvjOdPX4jgIKhUmzoaI6Ojs7xsUqVnGMV98TERJo2bcrRo0dzXYAiY+gjQFKajucX/wJApQA/0wcWWPyhFcX0h7yeYEp4QRpPvDw9H5jLy6go3Eo0vSFKFfNGhcr8xrH8B1O4k2zaf0k/L1SQ7T+qokBimikZ+vtk+TzO8o9jVBRSdQYAfLw8sjymPFBWZzAd8XqoVZb7vG+/+Z19MjtGBW6nGknVW19ZDVU0K7zmE6y+TrLiw1jdUL4ztnj4YIRwEi+qDzLzncn5HhVjTV6z6Yg9I3nHxcURHR1NmTJlqFChgi1VOZyHh4d5uoQ7aQrR90yJMfqedcOKTOXT8iiTko+6rGBVOSvrckJPqY8T4bWYEqoUriqlGaJ9mzNKNauem/FlIeM7g0oxvQ4qdca/uoJapTYXMpfTm/5GKi8/QPVgPSrTx58q7R6ggE8J8zcTlcqyvKIoqFPiTR/lmtKozV1YGeVN33LUienfFouXM8eUtR6VSoXRaESdkL4ma8lHUKnVqFSgylIXmEaN+SReRYWCMbA6HmqPB9uoUqEYjajjzpnKla5jLpf1S5ZiNKC+aZqpVSlbz/z+yIzN1F6jwYDH9ROAgrFCI3MX3P2vnWI0oI7+HyrAWOkJPDw9zPFn/FIMejyu/mq6XbkVag9PVCpISUlBo/Ez79dg0ON5+RAAxmpt8fD0yvJ6ZVZpMOjxjPrBdLvG03h6ZnyrtnzdjAYdHuf3oELBUOtZPD29MutJ/23Q6/E8azqnY6zbFQ9Pr8y/PZll9TodXn9vQ4WC/rEeeHl5Z/kbqLKU0+J3egMve3wPTKYg2JTYExMTGT9+PPv37wdM/zAtW7bk448/dunL/kv4ZXapfPFqM/y80/+h07dl/KHTdEb6rjRd3j+38yPUrFblgT90mt5A709Nc9tseaMlvl6Zc+1kfYOk6vX0WGL6h/56eCv8vDLfHFn3m6Iz0HWR6dvEzhFt8PPKeLNl7jdFp+fZhabVj/aMehI/L0+LN6xKZSqfotXTfoHpzbF/TCh+3h4PJIpLly5RvlJlQuf+CMCh8U+h8fY078tiv0kJaD55HBWQ/OYpNMVKPPDmTk1KQBNhWuEpZeQ/pjL37fPmzZvsmtaVwRXPmdpcthHl+m3iG/8yD+wzOeku/vOqolJB8tgrOY4/1syrYrqdXiancez3l8uOTeXetiK2kXnV9aLp9ls5l/vr1Anqb+1tKjcsr/q6mW4PzS229DKD86rredPtV/Mq19V0e1Bu++xiut3/Sq7j2DXzTN0dyX2H5bHPZ023w0fkUa6T6Xavt3OJraPpdo9xudd1YYnpdrdJuZc795npdrYlHp5NiX3+/PkkJyezc+dOHnnkES5fvswHH3zA3LlzmTlzpr1jdIiQaoHmRHa/rJeA1y3jR4MqgbmWeaxiCavqql2uuFXlqpf2z7Zc1jKVgzRW1VW+pG+25e5pPClT3Md8v3Qxnxzr81G80KhMF8h4+Xmi8XvwnIOH3hONyvTNRuXt8UBd8bdu8ucH7RlS6V/zNuXlLXiVyP5CJnWWo2QhhCWbhjseOHCA+fPnU6NGDXx8fKhduzZz5851z0WuRYFLiIvlwpzWdC39r+UDHnIFkBC2sCmxp6SkULx4cYttJUqUeGB6AeHitElc8u3LJd++FpfT21PyzcvcnNecJ4rFojWquNZyRoHsR4iixKbE3rBhQxYuXJg5ekJRWLhwIQ0aNLBrcMK9aaNPkbywJTV8bnNX58GNTiso2foVR4eVo6zdRzl1SwnhDGz67xw7diz9+/fn22+/pVKlSkRHR6NSqVi9erW94xNuSn9+P/q14ZT21HI12ZvUF9dSs3kni4mUCoskbOFubPovrl27Nnv27OGHH34gLi6OSpUqERoamuu4SiEyqE5tht3j0KiNnLrjhxL+JY83L5irjB1BPiiEo9n8XxcQEMCLL75oz1hEEeG3Zwyo4PtrxSnefx3NWz5VIPuRBCuKKoeuoCSKEIPO4u7nF4JQ915D8zZPOSYeIdyYJHZR8NIS8dn2qvnuh6fLUabPJ4Q93d7mKuVoXIicyTuiKEofxgiQrL0C3gW4gEniDVjfC49rf5o3VekxzW5TMLsq+WASBcmmI/b3338/2+3jx49/qGCEm7kVCSvbw7U/uaPNnFLh+a5dHBhUEeftT7XUL6mW+mXOC7BbU6Ygygm7sfpQITY2ll9/Nc1psnnzZurXr2/x+L1798zT+ArBv3/Al70hJZ6YVF9G/VGRTU9G5fk0OZK1jeLpZ0qcwBlXTJ7pyR9cNH4nY/U7JzAwkHXr1hEfH49WqyUiIsLicR8fH9588027Byhc0NldsPkV0KcQlRbA0EOBlHqkBpB3YheWNMVKwnTT2H5NHmWFyGB1Yvf29mbLli0AvPbaa3z22WcFFpRwXR4n1sP3k0Exck6pymv7PfHyD2Du7PdhrekbnRyJY+qSmF74F2O5PDmyt4pNfeyfffYZmzZtIjY2FoBvv/2WzZs32zUw4Zp89k0CxUhk8Zb0/86LNMWTOXPmULlyZUeHJkSRYdOhU0REBNu2baNZs2YA+Pv7s3DhQu7evcvrr79u1wCFC7hvjPqV4L70WfQHiqJi3Ngxpv+TAppEzCnJ0bjjFfEje5sS+5YtW1i/fr35KOzpp5+mVq1aDBw4UBJ7UaNNQvPtEPPdW80m0v+/u1EU6NatG71793ZgcEWHn5eaS3Oec3QYwknYvILS/UvhVahQgeTkgloPRDilpDj4shdEHzVvevOzX0lKSqJx48ZMmDAh24WthRAFy6Y+9nr16rF8+XKLbatWraJu3bp2CUrYqBDmTze7fQlWdTQldb/MFaSuXr1KhQoV+PDDD/HyenAlJZeW0cUy/a6Mx3YXbjrG3qYj9okTJ/Lqq6+yadMmypcvz/Xr19Hr9axcudKmIOLj4wkPD+f999+nefPmAOzZs4clS5bw77//EhAQQI8ePRg2bBhqtcyCUFhyHFN+7QSs7wWJsVCyCoSvheWhAPj5+rFgwQICAx9cLlA4nsbbU7psigCbEnu9evXYu3cvBw4cIDo6mipVqtCuXTubpu09evQoEydO5MqVK+Ztf/31F+PHj+fjjz8mNDSUixcvMnjwYDQaDa+++moutYkCF/UjbOwH2ntQrgG8vJndPx6mU/rDU6ZOoVatWo6MUIgiz6bDX71ez6pVq5g5cyYrVqygYcOGDBw4kBs3buSrnm3btjF27FhGjx5tsT06OpqXXnqJdu3aoVarqVGjBh06dOCPP/6wJVxhL6e2wLqepqRe7Ul4ZSdX7uiYP3+BuUho27YODNBG0sUicuOC3TU2JfZFixbx22+/ERERgZeXF6VLl6Z8+fLMmjUrX/W0adOGffv2PTAh1DPPPMOkSZPM91NTU/nxxx+pV6+eLeGK+9h02f7vK2Dra2DUQb3u0G8rOg8NkydPJiU1pYAiFULYwqaumO3bt7NhwwbKlSuHSqVCo9Ewe/ZsOnTokK96ypQpk2eZxMRERo4cia+vL4MGDcp3rFFRUWg01l2MnaLLXIz7woUo/Lyy/9zLWi45OYnIyEi71PWw5VJTksiYwSfq4iV8/bI/ulDpU6iRUVdUFIqn3wNlkpKSuBAVZS7H99MAuFPnJW41GgOX/mXNmjX8+eeflC6Z+frmVJ81+7R3uezKJCVl//dydfZslyP+d3Mqc3+7nCm2+1n7/rO2XHasHXloU2JPTk4mKCgIwLygta+vr91PbEZFRTFixAhKlSrFF198YVMffnBwsNXPS9bqgfMA1KgRnOPRbNZyGo0/NWvWtEtduZZLvMsl3ydMtytfMc0hkk2ZDMHVq2VbBrAYMVMjODjbr5eRkZHUqFTKcmP7GQS0HkmASsVvv/3G7t278fHxYcaMGfBTeK71WbNPu5fLpkxkZGS2fy9XZ+92XZpTO88y9vwfz6nM/e2y+/vqIWLLrpz5oqg69XJ9L2fI9X2ajcTERKvK2ZSJGzVqxOLFiwHM45TXrl1LgwYNbKkuWwcPHqRXr148+eSTfPbZZ5QsWYBzhrsJe86MqNIlwaaBmRu6LoQ2o0ClIj4+nnfffReAF198kbZPPvlQ+xJC2JdN7/533nmHQYMGsW3bNpKSkujcuTNJSUmsXr3aLkH9+eefDB8+nOnTp9OzZ0+71CnyIfEmlfYNhfi/M7c16AWA0Whk+vTpxMfHExwczNtvvw3oHRNnXuTSfuGMCmG6g3wl9oMHDxIaGkqVKlXYuXMnBw4cICYmhvLly/PUU0/Z1FWSnWXLlqHX65k1a5bFCdmmTZvaPFZeWCk+Ctb2wPf2RfALgpR4i4c3btzI4cOH8fb25oMPPsDHxwe0TprYhSii8pXYx44dyx9//EHHjh3Zu3evXZc3O3v2rPn2smXL7FavuyiUBShi/oT1PSHpJrpilfDq93+wrI354bNnz7Jo0SIARo0a5ZZ91cJ6crGT88pXhvDy8mLWrFnExMSY+9jvJ4ttuKgL++H/+oM2Eco34GrreVQPCjY/nJKSwjvvvINOp6Nt27b06tXLgcEKIXKTr8Q+depUNm/ejNFo5MiRIw887vITPhXmIs/O5OQm+Po/YNRD9VAIX4fhquXFZhEREVy+fJkyZcrw7rvvuv7fWgg3lq/ErtFoWLVqFb169WLt2rUFFVORoiHN/GECMdh4Ptt2Rz6FH2aYbtd/EbotBU8fwDKxb9+xA5XKg5kzZxIQEFC4MWZHTowKkSOb+tjv3pU3lNvISOothkHHWZDLtQiDBg0iJCSkkAIT7kL64gufTX3s165dkz52V2bQWt7v8B60GgH3da8YjUbzhQ6PPfYYQ4cOLZz4hBAPRfrYi5rUBNNJ0gxdI6DpwGyLbt6yhfD029OnTcPTUxahFsIV5Oud+uyzz/Lss89KH7sVnPLr571YWP8iXD+Vua1B9heAxcbGsmL5CsLDTPcrVqxYCAEKIezBpkOwzZs3m2/Hx8eb540RTuxWJKzrDneugKY0JN/KsaiiKCxbtozUtNRCDFAUZU55IOTCbJ6P/aOPPqJp06aEhYVx5coVXnzxxXzPxy4Kyb9/wGcdTEk9sDoM/DbX4rt27eLPP//E29u7kALMQuZGF+KhPdR87AsXLnyo+dhdVcbRxaU5z+U4hafTOLsbPu9qmhqgYmN4bR8EVsuxeHx8PPPmzQPgFRumSRZCOJ5NWWn79u1ERETQpk0bi/nYf/vtN3vH57q0STC9pOmnoBeWzsnRz2FjH9CnQM32MHAHFMt9Dvz58+eTkJBAtWrV6NOnTyEFKoSwJ6eej91paZPgA9PJRNVLPzs4mBz8tAB+Mh150+hl07S7Hl65PuXnn39mz549qNVqhg0bJqNghNORvnjrOO187I5QKBNtFZaMpP7kWHjhkzyTenJyMrNnzwagb9++siC1EC7Mpuw1efJkBg4cWGDzsQsb6bIum6WCznOh2WCrnrp48WJiY2OpWLEiQ4cOJTo6umBiFEIUOJsSe+XKldm5cyc//vgj0dHRdp+PXdgg6Ras7515v8cKeNy6GRhPnjxpHsI6ZcoU/PyyX2dUCOEabO5v8PPz49lnn7VnLMJW8VGw7kXT7wx1rZsrX6vVMnPmTBRFoWvXrjRr1qxgYpRJu0QhKup98UXkbKcbiz4KKzuYknrJR/L99HXr1nPx4kWCgoIYPXp0AQQohChskthd2bk9sKaL6SrS8o/DwO35ruKLtV8AMG7cOEqUKGHvCIUQDuDiQz+KsKOfw47RoBigRhj0/gJU+f+c1uv1tG37FO3bty+AIIUQjpCvxN6/f/88Z3D84osvHiogYYVDc+Hnj0y3s45Rt+FCKI2fHxMnTpSZOUWR5K598fk6xGvevDnNmjWjYsWKnDlzhkcffZRnnnmGhg0bcvbsWapXr15QcYqsMpJ62/FWjVG/3624OPPt/wwbRtmyZe0ZnRDCwfJ1xJ6xiEbfvn1Zvnw5TZo0MT/2zDPPMHXqVPtGJzKlJWbeVqnhuQUQ8opNVS1dupSp6R/pLzz//MPHJiNehHAqNp08/fvvv2nYsKHFtjp16nDp0iV7xCTul3DNNOVuhp6rbU7qx48fZ8+ePeb7RWYaCCGKEJve1TVq1GDNmjUW25YtW0bdunVtDiQ+Pp4OHTo8sDLT8ePHXX6qgocSewZWtofY05nbanWwqSqDwcCHH35op8CEEM7KplEx77zzDm+88QZr166lfPnyxMTEYDQa+eyzz2wK4ujRo0ycOJErV66YtymKwtatW5k1axZarTaXZ7uxqIPwf/0gLQGCakD8hYeqbuvWrZw/f56yAcXtFKAQ7s8VT7DadMTepEkT9u7dy8iRI2nXrh1jxoxh165d1KlTJ991bdu2jbFjxz5wccw777zD5s2bGTFihC0hur4TG01Xk6YlQJWWeS6OkZf4+HiWLl0KwOAhQ+wRoRDCSdk8jj0gIIDu3bs/9NJ4bdq0oWvXrnh6elok95EjR1K+fPlsF812a4piGs54IH3Rkno9oNtS03j1h7B48WLu3btHnTp1TCdMzwyzQ7BCCGdkU2LX6XQsXryYdevWYTAY2L59O6NGjWLp0qX5HjpXpkz2Cz+UL1/eltAeEBUVhUajsaqsSp9CjfTbF6KiUDyznwwra7nkpCQiIyPtUteFyHOUOfoRJS58A8DtxwYS1+hNuHzVqvpyKnPu3DnzJF99+vTh4qVLedaVlJRE5JVr0O+oacOVa9nG72qScvh7uTppl+Ol6Izm2xcuROW4upq15bKTnJycdyFsTOyLFy82L403evRoSpUqZV4ab+HChbZUWWCCg4Otn3UyywU+NYKDc15zM0s5jb8/NWrWtEtdNf6YBhcPmoYzdp5L4BOvE5if+rIpYzQamTFjBj4+PnTp0oUuXbpYVVdkZCQ1s2uXi5N2uRZXa9elObXzLJOs1QPnAahRIzhfaz8kJibmXQgbE/v27dvZsGED5cqVs1gar0MH20ZriHQXD4KXxjScsU4nu1T59ddf8/fff+Pv7190z1cIUcTI0niOlnUYo38Z6LsJKjXJuXw+3L1717zS1RtvvPFQ50KEEK5DlsZzpHN7YW23zPsDt9stqQMsWbKEhIQEatSoQe/evfN+ghDCLdg8jn3QoEF2Xxrv7NmzD2xr3rx5tttd3pHlsHsCKJknUgioYrfqz547x1dffQXAhAkT8PDwsFvdQgjbZYyLj4yMLLC1lW2qtUqVKuzcuZMDBw4QExMjS+Plh9EAe96BI8tM9xu+ZBqzbmcLFixAURQ6depkMaePEML92dQV8/777+Pn50fnzp15/fXX6dKlC8WKFWP8+PH2js+9pCXCxr6ZSf3padB5foHs6vTp02g0GkaOHFkg9QshnJfVR+yxsbH8+uuvAGzevJn69etbPH7v3j327dtn3+jcSUIMfBkO10+Cpy90Xwb1uts0h7q1Bg8enON1AkII92V1Yg8MDGTdunXEx8ej1WqJiIiweNzHx8c8ra+4z/VTsHkQ3LtmGvny0gao/ESB7rJqlaq89NJLBboPIYRzsjqxe3t7s2XLFgBee+01myf8KpLWdgddMpSuAy9vgsBqBbKbS5cuk1HzqFEj8fLK3wIcQgj3YNPJ05ySul6vx9NTllEFTHO+ZNAlQ/VQ07qkfgEFtDuFRYsimF/KdP+JJ3L5RiALYwjh1mzKwleuXOGTTz4hNjYWo9E0XE+n03Hx4kV+++03uwbokvRa2JXlRHKjvtA1It9L2OXHL7/8wpHff4dnC2wXQggXYdOomMmTJxMdHU3x4sXR6/XUrl2b8+fP069fP3vH53qS401dL3+uz9z27NwCTeo6nY4FCxYUWP1CCNdiU2L/66+/+OSTTxg2bBjFixdnypQpLFiwwDxqpsi68Q+saAeXfwbvLGP606/OLSibNm3iypUrBAUG5l1YCOH2bErsfn5+lCxZkipVqnDu3DkA2rZtS1RUlF2Dcynn9pqWsLt9CQKqmqYHKATx8fGsWLECgKFDhxbKPoUQzs2mxF6lShUOHjyIv79pWth///2X2NhY9Hq9veNzfooCv34CG8JBew+qtobBB6BM/leTssWyZctITEykbt26PPusdLALIWw8eTpkyBBGjBjBjh07CA8P56WXXsLDw4OwsDB7x+f0yvzxX4jaYbrTuD88twA8vQv0wqMM586dY9u2bQCMHTtWZtcUQgA2JvawsDD27t1LqVKlGDZsGNWqVSMxMZHu3bvbOz6nVyJqh2lhjI6zoMV/Crw/PYOiKMybNw9FUejYsSONGjUqlA8TIYTzsymx37lzh61btxIdHW0e7ghw/PhxZs+ebbfgnNaNv803DV7+ePT+HGoV7iIjBw4c4NixY3h7e8sCGkIICzYl9lGjRnHt2jUaNWpU9L7+n9oC32ZOnRDd/lOqFHJS12q1fPzxxwAMHDgwc31YufBICIGNif3EiRMcOHCAgIAAO4fjxAx6+H4a/LrYYrOuZLVCD2Xjxo3ExMRQtmxZBgwYUOj7F0I4N5tHxeh0OnvH4rySbplWOspI6i0dO9nZ2rXrABgxYgR+fn4OjUUI4XxsOmJ/9913GTJkCN26daNkyZIWj3Xr1s0ecTmP6KPwfwMg4arpoqNuS6Bm+weO3AtTSmoKjz/eiGeeecZhMQghnJdNiX3Lli2cO3eO1atXW/Sxq1Qq90rsx9bCzjFgSINSNSF8PZSt6xSjT8aOHWteb1YIIbKyKbHv3r2bb775hpo1a9o7HuexawIcX2u6XaezaWEM35K5P6cAGY1Gc79Z52c789hjjzksFiGEc7MpsQcGBlKliv0WXnZKx9cCKmg3GZ4cAw4e/bP3x1+YsqMWGo2Gr74a5dBYhBDOzabEPmLECCZNmsRrr71GyZIlLboEKlasaLfgCp0uOfO2b0nosRJqd3RcPOnS0tJYtGgRAIMGDaJ06dIOjkgI4cxsSuwTJ04EYOfOneakrigKKpWKv//+O7enOrksfdaDvoPy9XMuWojWr19PbGws5cuX5+WXX3Z0OEIIJ2dTYv/hhx/sGkR8fDzh4eG8//77NG/eHDCNlX///feJjIwkMDCQ//znP/Tq1cuu+32AV5ahg0HVC3ZfVoqLi2PNmjUAvPXWW/j4+Dg2ICGE08tXx/H169cB0+iXnH7y6+jRo4SHh3PlyhXztrt375qHU/7xxx/MmjWL2bNnc/LkyXzX7+qWLVtGcnIy9evXp2NHx3cLCSGcX76O2Dt37syxY8cICwt7IInb0hWzbds2IiIiGDduHKNHjzZv37t3LwEBAeZuh5YtW9K1a1fWr1/P448/np+QnV8u0wCcP3+eb775BoC3335bhjcKIaySr8S+c+dOwH5dMW3atKFr1654enpaJPbz589Tu3Zti7I1a9Zky5YtdtmvK1AUhY8++gij0UiHDh3c7wNNCFFg8pXYK1SoAMDq1auZMmXKA4+PHz+eDz/80Or6ypQpk+32pKSkBy6V9/X1JTk5OdvyuYmKikKj0VhVVqVPoUb67QtRUSie2V+un7VcclISkZGRNteVk6NHj/LTTz/h6elJly5dst1HQUrKoV2uTtrlWty1XWBb26zNgVYn9tjYWPOapps3b6Z+fcsRI/fu3WPfvn35CDFnfn5+3Lt3z2Jbamoq/v7++a4rODiYYsWK5V0QLK4orREcbOomyaOcxt+fGtldqGVtXdnQ6/VMnDgRHx8fBgwYQOvWra1+rr1ERka65QVo0i7X4q7tAtvalpiYaFU5qxN7YGAg69atIz4+Hq1WS0REhMXjPj4+vPmmfSbHql27Nr/88ovFtsjISGrVqmWX+p3dtm3buHTpEgEBAbz66quODkcI4WKsTuze3t7mPu7XXnuNzz77rMCC6tChA3PnzmXNmjW8/PLLHD16lO3bt7NkyZIC26ezuHfvHp9++ikAb7zxhvXfNoQQIp1N18l/9tlnGAwG8/2DBw9y6tQpuwUVGBjIqlWr2L17N82bN2fKlClMmTKFFi1a2G0fzmr16tXcuXOH6tWrF8mlBoUQD8+mC5T279/PlClTOHz4MEuWLGHZsmWoVComT55M7969bQrk7NmzFvcbNGjAxo0bbarLVUVHR7NhwwbAtEqVh4eHgyMSQrgim47YlyxZwqhRozAajaxbt45Fixaxfv16VqxYYe/4ipRFixah0+lo3rw5rVq1cnQ4QggXZdMR+7///kvv3r05c+YMKSkptG7dGk9PT27dumXv+IqMEydO8P3336NWqxk9erRcjCSEsJlNid3Pz4+4uDj2799P06ZN8fT05J9//iEwMNDe8bmufCwsbTQaWbBgAQAvvPCC2w7vEkIUDpsS+4svvki3bt1ISEggIiKCv/76i9dff12G5tlo7969nD59Go1GwxtvvOHocIQQLs6mxP7WW2/RrFkzfHx8aNSoEdeuXeO9996TSapscP9c66VKlXJwREIIV5evk6dHjx41327evDmNGjUCTFMNdOzYsUDHtrurDRs2EBsbS7ly5WSudSGEXeQrsQ8ePNji/gsvvGBx/5NPPnn4iIqQ27dvs2rVKgCGDx8uc60LIewiX4ldURSL+zExMbk+LnK3YsUKkpOTqVu3Lp06dXJ0OEIIN5GvxH7/ELy87oucXb582TxFw6hRo1A7eLFsIYT7kGziIBERERiNRtq2bUtISIijwxFCuBFJ7A5w7NgxDh48iFqt5q233nJ0OEIIN5Ov4Y56vZ6vv/7afF+n01nczzoxmMie0Wjk448/BqBHjx5Ur+4ci2YLIdxHvhJ76dKlLeZhDwwMtLgvY7DztnfvXs6cOYNGo2HIkCGODkcI4Ybyldj3799fUHEUCVqtlsWLFwOmi5GCgoIcHJEQwh1JH3sh2rhxI9evX6ds2bL07dvX0eEIIdyUTVMKFHlZJvhSrFyM9s6dO+Yrc4cPH46vr2+BhSeEKNrkiL2QrFixgqSkJGrXrs2zzz7r6HCEEG5MEnshuHLlivlipNGjR8vFSEKIAiUZphAsWrQIg8FAmzZteOKJJxwdjhDCzUliL2DHjx/nwIEDqNVqRowY4ehwhBBFgCT2AmQ0Gvnoo48A6NatG8HBwQ6OSAhRFEhiL0Dff/+9+WKkoUOHOjocIUQRIYm9gKSkpLBw4UIABg4cKFflCiEKjVMn9gsXLvDaa68REhLCU089xdKlSzEajY4OyyqrV68mNjaWChUqyMpIQohC5bSJPSkpiddff50KFSpw6NAh1q9fz3fffceSJUscHVqerly5wtq1awEYM2aMXIwkhChUTpvYjx49SlxcHO+++y4ajYZKlSrxn//8hw0bNjj1Sk2KojBv3jx0Oh2tWrUiNDTU0SEJIYoYp03sRqMRLy8vvLy8zNtUKhW3bt0iISHBgZHl7tChQxw+fBhPT0/Gjh0rq0oJIQqd084V06RJE3x9fZk/fz7Dhw8nPj7ePNdKamoqJUuWtKqeqKgoNBqN9Tvud9T0+8o1q4onJSURmT5fTFpaGu+99x5paWl06dIFrVZrfszVZG2XO5F2uRZ3bRfY1rbk5GSryjltYi9RogQrVqxg9uzZPPXUU1SpUoVu3bpx6tQpSpQoYXU9wcHBFCtWrMDijIyMpGbNmgAsX76cu3fvUrlyZSZMmICfn1+B7begZW2XO5F2uRZ3bRfY1rbExESryjltYtdqtej1er744gtzd8aXX35JzZo1nTJhRkdHs2bNGgDefvttp4xRCFE0OG0fO8Brr73Gli1bUBSFv/76i2XLljFw4EBHh5Wt+fPno9VqadasGU8//bSjwxFCFGFOm9i9vb1ZsmQJGzZsoEmTJowaNYrBgwfTu3dvR4f2gF9++YVDhw7h4eHBuHHj5ISpEMKhnLYrBuCJJ57gq6++cnQYudJqtcydOxeAvn37yuLUQgiHc9ojdlfxzTffcPXqVcqUKcPgwYMdHY4QQkhifxjXrl0zL6AxatSo/A2rFEKIAiKJ/SF89NFHaLVamjRpQseOHR0djhBCAJLYbfbbb7+xf/9+1Go1EyZMkBOmQginIYndBjqdznzC9LnnnqNGjRoOjkgIITJJYrfB8uXLuXz5MkFBQYSHhzs6HCGEsCCJPZ8OHjzI6tWrARg/fjz+/v4OjkgIISxJYs+HK1eu8O677wKmMevt27d3cERCCPEgSexWSklJYdy4cSQlJdGoUSNGjBjh6JCEECJbktitoCgKs2bN4sKFC5QqVYo5c+bg6enUF+0KIYowSexW2LRpE7t370atVjNnzhxKly7t6JCEECJHktjzcOLECRYsWACYri5t3LixgyMSQojcSWLPRVxcHBMmTMBgMNCxY0f69Onj6JCEECJPkthzoNfrmTRpErdu3SI4OJgpU6bI1aVCCJcgiT0Hixcv5tixY2g0GubOnSsTfAkhXIYk9mx8//33rFu3DoDp06dTtWpVB0ckhBDWk8R+n4sXLzJjxgwABgwYQFhYmIMjEkKI/JHEnoXRaGTcuHGkpKQQEhLC8OHDHR2SEELkmyT2LBITE4mJiaFcuXJ88MEHeHh4ODokIYTIN7l8MosSJUqwdetWihcvTrFixRwdjhBC2EQS+30qVKjg6BCEEOKhSFeMEEK4GadO7KdPn+bll18mJCSENm3a8P7776PVah0dlhBCODWnTexGo5GhQ4fyzDPP8Pvvv7NlyxZ+/vlnVqxY4ejQhBDCqTltYr979y43b97EaDSiKAoAarUaPz8/B0cmhBDOzWkTe2BgIIMGDeK///0vDRo0IDQ0lGrVqjFo0CBHhyaEEE7NaRO70WjE19eXqVOn8ueff7Jjxw4uXLhARESEo0MTQgin5rTDHfft28eePXvYvXs3ALVq1WL48OHMmjWLUaNGWV1PVFRUgU7glZSURGRkZIHV7yjSLtci7XI9trQtOTnZqnJOm9ivXbv2wAgYT09PvLy8rHp+Rr982bJlC/Rio6ioKMqXL19g9TuKtMu1SLtcjy1tS0xMBDLzW06cNrG3adOG+fPns2zZMgYPHkxMTAxLly6la9euVj0/KSkJgNDQ0IIMUwghCl1SUhLFixfP8XGVklfqd6DDhw/z8ccfExUVRfHixXn++ecZPnw43t7eeT7XaDRy48YN/P39ZYEMIYRbUBSFpKQkypYti1qd8ylSp07sQggh8s9pR8UIIYSwjSR2IYRwM5LYhRDCzUhiF0IINyOJXQgh3IwkdiGEcDOS2IUQws1IYrdBXFwcw4YNIyQkhObNmzNr1iz0er2jw3oo8fHxdOjQgSNHjpi3nThxgl69etG4cWPCwsLYvHmzAyPMn3/++YdXXnmFZs2a0bp1a8aPH098fDzg2u369ddf6dWrF02aNKF169bMnDmT1NRUwLXblcFgMNC/f38mTpxo3ubq7fruu+947LHHaNy4sfln3LhxQAG2TRH51q9fP2XMmDFKcnKycuXKFeW5555TVqxY4eiwbPa///1Pad++vVK7dm3lt99+UxRFUe7cuaM0a9ZMWbdunaLT6ZTDhw8rjRs3Vk6cOOHgaPOWkpKitG7dWlm4cKGSlpamxMfHK4MHD1aGDh3q0u2Ki4tTGjRooGzdulUxGAxKbGys0qVLF2XhwoUu3a6sPv74Y6Vu3brKhAkTFEVx7f/DDHPmzFEmTpz4wPaCbJscsefT5cuX+f333xk3bhx+fn5UrlyZYcOGsX79ekeHZpNt27YxduxYRo8ebbF97969BAQE8PLLL+Pp6UnLli3p2rWrS7QzJiaGunXrmqefCAwMJDw8nD/++MOl2xUUFMThw4fp0aMHKpWKO3fukJaWRlBQkEu3K8Ovv/7K3r176dixo3mbO7Tr1KlT1K9f/4HtBdk2Sez5dP78eQICAihXrpx5W40aNYiJiSEhIcGBkdmmTZs27Nu3j86dO1tsP3/+PLVr17bYVrNmTf7555/CDM8mwcHBrFy5Eg8PD/O2PXv2UK9ePZduF2CeqTQ0NJSuXbtSpkwZevTo4fLtiouLY/LkycyfP99ilTRXb5fRaOT06dP8+OOPtGvXjrZt2zJ16lTu3r1boG2TxJ5PSUlJDyzPl3Hf2rmSnUmZMmXw9Hxwks/s2unr6+tybVQUhY8++ogDBw4wefJkt2nX3r17OXToEGq1mhEjRrh0u4xGI+PGjeOVV16hbt26Fo+5crvAdO7qscce45lnnuG7775j48aNXLp0iXHjxhVo2ySx55NGoyElJcViW8Z9f39/R4RUIPz8/Mwn5TKkpqa6VBsTExMZMWIE27dvZ926ddSpU8ct2gWmBFCuXDnGjRvHTz/95NLt+vTTT/H29qZ///4PPObK7QIoXbo069evp2fPnvj5+VGxYkXGjRvHoUOHUBSlwNomiT2fatWqxZ07d7h165Z524ULFyhfvnyu8yO7mtq1a3P+/HmLbZGRkdSqVctBEeXPlStXePHFF0lMTGTLli3UqVMHcO12HTt2jE6dOlksQKPVavHy8qJmzZou265vvvmG33//nZCQEEJCQtixYwc7duwgJCTEpf9eYBqdNW/ePIuFMbRaLWq1mscff7zg2vbQp1+LoD59+iijR49W7t27Zx4VExER4eiwHlrWUTHx8fFKSEiIsnr1akWr1Sq//vqr0rhxY+XXX391cJR5u3PnjvLUU08pEydOVAwGg8VjrtyuxMREJTQ0VPnggw+UtLQ05erVq0rPnj2VadOmuXS77jdhwgTzqBhXb9e1a9eURo0aKcuXL1d0Op0SHR2t9O7dW3nnnXcKtG2S2G1w8+ZN5a233lKaNWumtGjRQpkzZ46i1+sdHdZDy5rYFUVRTp48qYSHhyuNGzdWnn76aWXr1q0OjM56q1atUmrXrq00bNhQadSokcWPorhuuxRFUc6fP6+88sorSkhIiNKuXTtlwYIFSlpamqIort2urLImdkVx/XYdOXLEHH+LFi2UmTNnKqmpqYqiFFzbZKENIYRwM9LHLoQQbkYSuxBCuBlJ7EII4WYksQshhJuRxC6EEG5GErsQQrgZSexCCOFmJLELIYSbkcQuClxaWhrXr193dBjZcubYnJnRaKRTp07MnTvXYvtPP/1E/fr12bVrl4MiEyCJXdiof//+LFq0yKqyffv25fDhwwUckW2yxhYTE0Pjxo2JiYlxcFSZrl69Sp06dbh69apd633YtqrVaoYOHcqGDRu4e/cuYJrwauTIkYwePZpnn33WnuGKfJLELgrc7du3HR1CjrLGVrFiRY4fP07FihUdGFHhsEdbu3btSmBgIGvXruX69esMGTKEF154gddee82OkQpbSGIXDyXjiHLz5s2EhYXRtGlTXnnlFXP3xquvvkpMTAzTpk3jvffeA0xT6r7xxhs0b96cdu3a8dFHH5mnos2ob86cOTzxxBO8/vrrjBkzxmKfo0aNYsaMGVbXZW1s9x8dnz17lsGDB9OsWTPatm3L9OnTuXfvXp713m/8+PG5tmH//v289NJLtGzZkoYNG9KvXz8uXbqU42ud9eh90aJF5nnMc3stcqsrv+3J4OnpyeDBg1m7di2DBw/mscceY8qUKbk+RxQOSezCLn788Ue+/vpr9uzZw61bt1iyZAkAq1atomLFisyYMYN3332X5ORkBg0aRK1atTh06BBffvklhw8ffqBbJykpiV9++YU33niD77//nsTERAASEhLYv38/PXv2tLoua2PL6vbt2wwYMICaNWty6NAhtm7dysWLFxk/fnye9d6vd+/eObbh+vXrjBw5kiFDhvDrr7/y448/oigKn3zySb5ef2tfi9xY256sunbtal5oZsGCBRbLEQrHkcQu7GLw4MGUKFGC0qVLExYWlu0RJ5iSh1ar5e2338bHx4cKFSowcuTIBxbw7datG97e3oSEhFChQgXzybgdO3YQHBxMvXr1rK7L2tiy+uGHH/Dy8mLs2LH4+vpSpkwZpk6dyv79+82LrFhbb25tCAoKYufOnYSFhZGYmMj169cJDAwkNjY2zxhteV1zY8vrNHPmTMD0QShJ3Xk8uNilEDYoXbq0+banpyc5zQYdHR1NfHw8TzzxhHmboijodDri4uLM28qWLWu+3atXL7755ht69erFtm3b6NWrV77qsja2rOLi4qhYsaJFsnrkkUdsanNubfDy8mLHjh1s3LgRlUpF7dq1SUxMzHYd2tzk9VqUKlUqzzry+zp9/PHH/Pjjj2zatIlBgwaxZcsWXn755XzFLQqGJHZRqMqXL0+VKlXYvXu3eVtiYiJxcXEEBQURHR0NgEqlMj/evXt3Pv74Yw4fPszZs2fp0qVLvuqyRaVKlYiJicFgMJiT+5UrVwBy7LfOTU5t2LVrF+vWrWPDhg1UrVoVMB0Fnzt37oE6MuLQ6XTmbRknf/N6Lext8+bNrF69ms8//5y6desycOBAVq5cSe/evfHy8rL7/kT+SFeMKHDe3t7cu3cPgHbt2pGUlMTKlSvRarUkJCQwYcIERo8ebZHMswoKCqJdu3ZMmTKFjh07UrJkSZvryi22rEJDQwGYN28eqamp3Lx5k1mzZtGiRQubRpLk1IZ79+6hVqvx9fVFURQOHTrE119/bZG8M5QqVYqSJUuyc+dOFEXh9OnT5kRuj9fCWgcPHmTGjBnMnTuXRo0aAabhr4mJiXzzzTd23ZewjSR2UeB69uzJRx99xNixYylWrBhr1qzhyJEjtG3blvbt26NWq1m6dGmudfTu3Zvo6Gh69uxp3mZrXTnFllXx4sVZvXo1586dIzQ0lC5dulCpUiUWLlyYv8bn0Ybu3bvTqlUrnnvuOVq0aMHSpUsZOHAgFy9efOCbgbe3NzNnzmTXrl00adKEOXPm0Lt3b7u9Ftb466+/GDVqFOPGjaNjx47m7cWKFaNfv36sWLECg8Fg132K/JOl8YQQws3IEbsQQrgZSexCCOFmJLELIYSbkcQuhBBuRhK7EEK4GUnsQgjhZiSxCyGEm5HELoQQbkYSuxBCuBlJ7EII4WYksQshhJuRxC6EEG7m/wEqvY5eQOb1FQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 3))\n",
    "ax = fig.add_subplot(111)\n",
    "fig_ax = (fig, ax)\n",
    "\n",
    "X = [(1, 0)]\n",
    "Y = [(3, 0)]\n",
    "\n",
    "# Ground truth\n",
    "ax.plot(dox_vals, intervention_data[:, mask_dict[0][:,Y[0][0]]==False, Y[0][0]].mean(axis=1), \n",
    "    alpha=0.8,\n",
    "    color = 'black',\n",
    "    linestyle='solid',\n",
    "    label=\"Truth\")\n",
    "\n",
    "# Estimation using LinearRegression\n",
    "fit_predict_plot(X=X, Y=Y, graph=graph, dataframe=dataframe, \n",
    "                 estimator=LinearRegression(), \n",
    "                 dox_vals=dox_vals,\n",
    "                 data_transform=None, \n",
    "                 graph_type='stationary_admg', \n",
    "                 adjustment_set='optimal', \n",
    "                 confidence=True,\n",
    "                 boot_samples=50,\n",
    "                 seed=4,\n",
    "                 label='LinReg',\n",
    "                 fig_ax = fig_ax,\n",
    "                 verbosity=0)\n",
    "\n",
    "# Estimation using MLPRegressor\n",
    "fit_predict_plot(X=X, Y=Y, graph=graph, dataframe=dataframe, \n",
    "                 estimator=MLPRegressor(random_state=2, max_iter=1000), \n",
    "                 dox_vals=dox_vals,\n",
    "                 data_transform=None, \n",
    "                 graph_type='stationary_admg', \n",
    "                 adjustment_set='optimal', \n",
    "                 confidence=True,\n",
    "                 boot_samples=50,\n",
    "                 seed=4,\n",
    "                 label='MLP',\n",
    "                 fig_ax = fig_ax,\n",
    "                 verbosity=0)\n",
    "ax.legend(loc='best', fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis also demonstrates the importance to be more explicit and transparent in laying out assumptions that enable more robust conclusions and in assessing and discussing conclusions under alternative sets of assumptions. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-causal estimation\n",
    "\n",
    "Finally, let's see which results we get for non-causal models without covariates ('-uni') and with all other variables as covariates ('-all')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f87257aae50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gDPOOy3JkXP/Iy8sjJyeH7Ozsf3YqClpFKdLlcnpFjy4r677/rvV6/QOnQxo3bszKlSvZunUrjRs3BuC3336jU6dO/PXXX+Tm5pKVlUVubu5Dj6PX6w2vBfX7cvbsWdavX09kZOR9vy4rK4vBgwczZMgQpk2bRlZWFh999BFffvkls2fPRqfTGV535/ubnZ2d71gPiik7O5ucnBwuX76MvX3+PskFzYGFSuypqanMmzePefPm4eHhQdmyZfnzzz/5+eefycvLw83NjQMHDhTmkKSnpzN27FhOnDjB3LlzCQwMxMXFhbS0tHyvy8rKws3NrVDHBvUvg2JTttcY1auDVylY8hruZxfiXsYfWn5s9uQu5W2ty+OU7XVycspfHnbABsgp/EmUsewcXXF+wL/nh5XtdXV1pUOHDqxevdqQgDdt2mRocO3g4ICzszMODg7Y2dk98Dh2dnbMnDnTMNWrKAre3t60adOG4cOH4+TkdM/XODo6snDhQipVqkR2djZJSUl4eXlx8uRJnJ2d0Wq1gHpCeufr//19ftjYHB0dqVSp0n3L9hZEgRP7+PHjWbx4Me7u7gwZMoTu3bsbgs/OzubkyZOcPHmyoIcDICYmhoEDB+Lv78/ixYvx9PQEICAggB07duR7bXR0NDVq1CjU8W1GnW6QlQqr34YdEyEvB9qML5Izd/EE0mhAW/iTKEuwZM/TPXv2MHDgQDIzM6levToODg5F1tP0UQqc2Ldu3cp7771Hp06dcHR0zPeck5OT4WpyQaWmptK3b1/Cw8MZP368YY4MoFWrVkyYMIHZs2fTu3dvDhw4wMqVK5kyZUqBj29zGg5QW+utGQm7f4DcLGj7tcy5iyeapXqeHjlyhM8++4wFCxYQFBQEwMyZM7l48aJZ3r+wCpzY161bd898z+NYunQpcXFxrF27lnXr1uV77tChQ8ycOZPx48cTFRWFp6cn48aNIzw83GTvb5XCBoKDM6wYBvtnqMm9w2SwM93PRQhrc6fn6a1btx7Z8/Ruzs7OuLu7G/WeaWlp+aZ3Dh8+zJw5c8jNLR6r1wqc2E2Z1AH69+9P//79H/h8nTp1WLBggUnf0yaE9lHryywbDIfnQW42dJ4G9o6P/lohbFC7du3473//S9++fR/Z8/Rubdu25bvvvjPqPZs0acJLL71E79690ev1lC9fnj59+vDNN9+QmJho1DFNSXqePiaLXYw7uVxd367PhZrtoNsscNCa7PBykdG6SM9T6yM9T8W9anWEnnPVFnunV8FvL8sdqkIIQBK7dQt8Hl5cAA4ucG49/NpTvZNPCPFEK/QNSgDx8fFMnTqVS5cuodfr8z03Z84ckwQmCqj6s/DyYpjXAy5sVe9U7b0QnEpaOjIhhIUYldjHjh1LYmIiLVq0uGfpo7CAyk2hzzKY1w1idsKcTmqyd/GwdGRCCAswKrEfO3aM9evXG24oEsVAxafVkr+/dIbY/TCjjZrc3StaOjJRzNno+gmrZYqfh1Fz7CVLljTcdSqKkXKh0H8NlPSHxDMwvSXEHXr014kn0p2/to2pwSTM587P43FmQ4w6Yx8yZAhjx45l4MCBeHt753vO39/f6GCECZStDa9tUpt0JJyAWW2h+2wIaGPpyEQxY29vj7u7OwkJahtGV1dXNMWsTEW+AmU25t9jUxSFzMxMEhIScHd3f6x7h4xK7OPGjQNg48aNAGg0GhRFQaPRcOrUKaODESZSuhy8uhYW9oULW+DXXtB2AjR8zdKRiWLG19cXwJDci5ucnBybvY73oLG5u7sbfi7GMiqx//HHH4/1pqIIOJeG3otg5XA4PBdWvwMpMfDsx1JfRhhoNBr8/Pzw8fEhJyfH0uHc4/Lly1SqVMnSYZjF/cbm6Ohokrv8jUrs5cqVAyApKYnY2FjKlCmDn5/fYwcjTMzeETp+Dx6VYMt42DEJUq6oDTscbfNuPmEce3t7k5cNMQV7e3ubvfPUnGMzKrGnp6czevRoNm/eDKi/9Rs1asTEiRMpVaqUSQMUj0mjgYjRULoCrHgDTiyFtGvQaz64yqomIWyRUX+Tf/PNN2RmZrJ69WqOHDnC8uXL0ev1TJgwwdTxCVMJfhFeXgpOpSFmF8xoBcnFo8SoEMK0jErsW7Zs4ZtvvqFatWo4OTkREBDAhAkTTNLkWphR1QgYsB5KlYekaHU55KUH93QUQlgnoxL77du3KVky/y3rpUqVuqe8gCiGfJ5Sl0P61oXMRJjTEfb8aHSneCFE8WNUYq9Xrx6TJk0y3CGlKAqTJk2iTp06Jg1OmEkpP3h1PQR1U8v+rh0Nv/8Hcm5bOjIhhAkYdfF05MiR9OnThxUrVlCuXDliY2PRaDTMmjXL1PEJc9G6Qtfp4B8CGz+AI79CwknoOQ/cK1g6OiHEYzAqsQcEBLB+/Xr++OMPkpKSKFeuHBEREWZtaCHMQKOBxm+Abx1Y1A+uHYGfItQ7VZE7iIWwVkYldlDvjurataspYxGWUjUCXt8GC3rD9aMwpxOlQ4dDtQ/U5C+EsCpyC6JQuVeEARugbk9Q8ihz4BtYOgh0UiBKCGsjiV38w9EFOv8Iz/0XRWMPxxbCzNZw87KlIxNCFIIkdpGfRgPhg4l7dgq4esP1Y/DjM2rzbCGEVTAqsX/++ef33T969OjHCkYUH7d9G6jz7uXqQ1YqLHxFLSgmUzNCFHsFvngaHx/Prl27AFi0aBFBQUH5nk9LSzOU8RU2onR56L/u7wJiE+HALLUcQdcZ4Bv0yC8XQlhGgRO7h4cHc+fOJTk5GZ1OR1RUVL7nnZyceOONN0weoLAwBy20+gSqNodlr8ON0/C/SGgzXq3vLqtmhCh2CpzYtVotixcvBmDAgAHMmDHDbEGJYqhaC/jPTvUO1XMbYM1IOL8ZOnwPbl6Wjk4IcRej5thnzJjBwoULiY+PB2DFihUsWrTIpIGJYsjNG15aCM99CfZaOLMGpjWBi9stHZkQ4i5GJfaoqCimTp3K7dtqbRE3NzemTZvG9OnTTRqcKIY0Ggj/D7z2B3jVUGu7/9wB/vgU8opfBx4hnkRGJfbFixczZ84cKleuDMCzzz7LrFmzmDdvniljE8WZX1111UxIH0CBP79RywDHn7R0ZEI88YxK7Onp6fe0wvPz8yMzU5bCPVG0bmrrvW6z1B6r1w7Dj81g+wTIy7V0dEI8sYxK7LVr1+ann37Kt2/mzJnUrFnTJEEJKxPUBYbsgYDnQJ8Dmz+H6c9C/AlLRybEE8moImBjxozh1VdfZeHChfj6+nL9+nVyc3Nljv1JVsoPXlwARxeq9d2vHYYfI6D5u9BkuNpYWwhRJIxK7LVr12bDhg1s2bKF2NhYKlasSIsWLaRs75NOo4F6PdVqkSuHw9m16tn7qZXQaSqUrW3pCIV4IhiV2HNzc5k5cyZz584lLy+PFStW0LdvX6ZOnYqPj4+pYxTWpqQvvPgrHFsEa0apdd5/jICId6HpcDl7F1ZFl6snU5dLpi4PXa6e7Fz93x//eZx91+Ny7i48XdULezvL3bxnVGKfPHkyu3fvJioqiuHDh+Pt7Y2vry/jx49n0qRJpo5RWCONBur2gCrNYNUIdc37ls/h1AroMBn8gy0dobAxiqKQnasnI1tNwhm6XDKy88jM9zGXDF0emXc+3u81/3o+J6/w/YC9SzjRrq4fHYL9CangjqaI79A2KrGvXLmSX3/9lbJly6LRaHB1deWLL76gVatWpo5PWLuSvtBr/j9n79ePwv9aQNjr0OI9cC5l6QiFBej1Cpk5/yTQu5NxZvadj7nEXEvC5fyDk+4/r1U/6s3Yk93RXoOTgz1ODnZoHezu+miP1sEOrb0dDvYajsWmkpiezeydl5i98xIVPF1oX9efDsH+1PQtmn/vRiX2zMxMPD09AQwNrZ2dnbGzkyrA4j4MZ+8RsH4sHF8Ce6bCyd/h+f/CUx2k5owN0OsVEjOyib15m7iULGJTMolLyeLqzdtcS71NWlau4cz4dk5eIY6cWOhYXBztcdXa4+bkkP+j1gFXp3991NpTwskBVycH3LT2uGodcHPK/9FVa4+jfcHymy5Xz1/RN1hxOI4NJ+O5knybKVvPM2XreQLKlqBjcDna1zVv60mjEntwcDDff/89I0aMMPyJ8csvv1CnTh2TBidsTMmy0G0mBPeG1e/AzYtqOeAaraHtBPCobOkIRQHk5uk5l5DO0aspHL2aysXEDOJS1GSuy9MX6lgaDYbk6ubkgIvjnSSrJt3c7Az8vD3/SbRae1zvStZ3f+2d51wc7S06v611sCOyZlkia5blti6PTafiWXEkjm1nbnA2Pp0J688wYf0Z/hNehnermycGoxL7e++9R79+/Vi2bBkZGRm0bduWjIwMZs2aZer4hC2q/iwM2QV/fgt/facWFfvhT4gYDY2HycXVYkRRFC4nZXLk7yR+5EoKJ+JuPfCM204DZUs5U87dBX93F8p5/P3R3ZnSLlrc/k7YLn+fPTs72j10/jk6Oprq1c2U/YqAi9ae9vX8aV/Pn9TMHNaduMaKI3HsOp9EenbhfgkWRqES+7Zt24iIiKBixYqsXr2aLVu2EBcXh6+vL82bN5fljqLgHF0g8n2o0x1Wvw2X/oQ/PlHXwbf7Dio1snSET6SM7FwOxaSw71IyB2NucvRqKqm3760BVMLJgaBypahX3p1A35KGRO5b2rnAUxZPmtKujvRsWJGeDSui1ytcuHDebO9VqMQ+cuRI9u3bR+vWrdmwYQNt27Y1V1ziSVEmAPquhKO/wfr34cYpmPUcBL8MLT+CErJ81pwS0rI4cOkmey8ls//STU5eu0Xev65Aau3teMq/FMHlS1O3vDv1KpSmqncJ7Cw43WHtzP29K1Rid3R0ZPz48cTFxfH999/f9zXSbEMUmkYD9Xqpc+2bPoaDP8PhuerSyIjR6goaB62lo7R6iqJwMTGD/YZEnsylpHvrO5Vzd6FhZQ/qV/Yk+O8zcq2DnIVbk0Il9g8++IBFixah1+vZs2fPPc8X9VpNYWNcPaFDFIS8rJYliDsEG8bB/lnw3Bdq4pd/YwWWk6fnRNwt9l9KZt/fZ+RJGbp8r9FoILBsScKqeNKgsicNKnng7+5ioYiFqRQqsbu6ujJz5ky6d+/OL7/8YrIgkpOT6dmzJ59//jlPP/00AB999BFLlizB0fGfC2ljxoyhZ8+eJntfUUxVCIPXNsOR+bDpE0g+D/N7QPWW0Ob/oEygpSMsltKzczkUc5N9l26y/1Iyh2JS7rnIqXWwI7i8Ow2reNCgsiehFT0o7SIXq22NUXPsqampJgvgwIEDjBkzhpiYmHz7jx07xmeffUbnzp1N9l7CitjZqWfuT3WAP7+GXVMgehNc2Aphg9TyBC7ulo7SohLSsth/6Sb7LiWz48w1ziefvWd+vLSLIw0rq0m8YWUPgsqVxsnB3kIRi6Ji1Bz7tWvXTDLHvmzZMqKiohg1ahQjRoww7NfpdJw9e5agoKDChCdskXMpaPUphPZVp2XOrIHdU9SLrZHj1P12tp+oFEXhQmLG39MqajK//ID5cXVaxYOwyp5UKyMXOZ9EFp1jb9q0Ke3bt8fBwSFfYj99+jS5ublERUVx4MABSpYsSdeuXXnttdfk7tYnlVc1tbBY9CZY9x4knlFr0Oz9H7T8BGq0srn599u6PLaeSWDN8evsjE687/x4Td9ShFX2oLyzjnbhT+FXWubHRSET+/PPP8/zzz9vsjn2MmXK3Hd/WloaYWFh9OnTh2+//ZZTp04xdOhQ7OzseO211wr1HhcuXMDV1fWxY32QjIwMoqOjzXZ8Sym+46oMrX+m9NnFeB79EfuEkzC/O5ll65MU8hbZ3g8vDVx8x6XKytWz90oG2y+msTsmnazcf6ZWtPYaapZxJsjXhaCyrtQq60wJrfrXSkZGHhk3Yom+YanIzaO4/7wehzFjK2iXOo1yp9iLkZKTkw11Yx5HYGAgc+bMMVw8/bfp06ezZs0ali5dWqDjpaenU79+fQ4cOGDWG6es/c64B7GKcWUmq71W9/4EeX+fzdbuAs9+AJ5V7/slxXFcWTnqmfmqo9fYfDqBTN0/FzzLubvwQl0/WtcqS53yD54fL47jMgVbHRcYN7aC5jWj67FPnjw5Xz32ESNGmKwe+6ZNm0hMTKRXr16GfTqdDmdn58c+trAhrp7QZjw8/TpsHq/Ou59Yqjb2aPCqugbezdvSUT7Q/kvJ/LzrMn+cir9vMm9bx4965UvLMmJRaEZNWN+pxz5p0iQcHR3z1WM3BUVR+OKLL9i1axeKonDo0CHmzJkjSx3F/blXhC4/wuA/odqzat/VvT/CpGC1sbYuw9IR5nMy7havzt5Ht2m7WHkkjkxdHuXcXRjUrCq/D23CX++24L22TxFsgTrewjYUy3rsrVq1YuzYsXz88cfEx8fj7e3NsGHD6Nixo0mOL2yUbx3osxTOb4GNH6q13zd/Dnunq2fvIX0sGt6lxAy+3XiWlUfjUBSwt9PQo0F5ejasKGfmwqSKTT32M2fO5Hvcq1evfFMxQhRYtRZQZZta933zp5ASoxYa2zGRkk/1hypvgr1R//SNEn8ri6g/zvHbvivk/r3OvH09f95uFUAVb7cii0M8OYzKxHfqsQNSj10UT3Z2ULc7vLEfnp8AJcpCSgxld30CP4TBscWgN1/ZVIDUzBy+XHuaiAlbmLcnhly9QvPAMqwa1pTJL4ZIUhdmY9Rpy/vvv0/fvn2lHrso/hyc4OlB6l2s+6aTt/1r7JPPw5IB6oqaFu9BzXYmXQOfqctl1o5LTNt2nrSsXADqV/JgdJtAnq7qZbL3EeJBjErsFSpUYPXq1WzdupXY2Fipxy6KP60rNHmTS54RVEvYADsnQ8JJ+O1l8KsHLcY99k1Oulw9C/bFEPVHNInp2QDU9C3JqDaBRNb0kTl0UWSMnmh0cXHh+eefN2UsQpid4ugGEaMg7DXY9QPsngrXjsD87lA+DJq/q66sKUQSztMrrDgSy7cbz3Il+TYAFT1debtVAB3q+cst/aLIFd0VJCGKExcPtdbM04Nhx0S1NMHVvTC3K5RroBYZe8QZvKIo/HEqgQnrz3AmPg2AMiWdeDOyOj0bVjRpDXNFUcjJySEnJwedTkdOTg7Z2dmGx9HR0aSkpKC/67qBoiiGxQ13fw7g4OBg2BwdHR/40cXFJV+FVWEdJLGLJ5ubN7T+HBq9ATuiYP9MiN2vnsH7h6gJPuC5exL87gtJfLXuNAdjUgAo5ezA4ObV6Ne4Mq7a+/+3ysvL4+bNmyQnJxu2lJQU0tPTDVtGRka+j3c+v3379kOHkZ2djZOTk0m+Jf9mb2+Pi4uLYXN2ds732NXVlZIlSxq2EiVKUKJEiXz77uyXWk9FQxK7EAAlfeG5/4Omw2FnFOyboTb6+LUX+NZV18EHvsDxa2lMWH+GbWfVoizOjnb0b1yZF0PKkHHzBrv/2s61a9e4fv06SUlJ+ZJ4amoqj1nBw8DBwQGtVmvYcnJy8PDwMCTOO/P5/57X12g0KIpCXl4eOTk55Obm3rPd2X8n1ry8PMMvmcdhZ2dHqVKl8PDwwN3d3fDx7s3DwwMvLy+8vb1xd3d/rPd7khUqsffp0+eRF4DmzJnzWAEJYVElfNQz+CbD1Quse/+n3uj028tccazMDxkd2a5viB0aKuZcwf3yblZuOc+irKwCHd7Ozg53d3c8PT3x9PTE3d2dkiVL4ubmZjjTvbPd2efm5oarqyuOjo44OTnh4OBwz5mvOWqq5OTkkJWVxe3btw0f797u7Lvz10VaWlq+7c4vg7S0NG7fvo1eryclJYWUlJQCf69cXFyoUKEC3t7ehoTv5eVFmTJlKFu2LD4+Pnh6espfAv9SqMR+p0DX1atX2bRpE127dqVixYpcv36dhQsX8txzz5klSCGKyq1bt7h06RIXL17k0iVPYhM7U1MTTfeSh6mQc4mp2kmc03kz+1wpNl2yI13550THy8sLPz8/w+bt7W1I4HcncmtJQo6Ojjg6OlKyZMnHPlZOTg63bt3i5s2bpKSkGD7++/Pk5GSSkpIM1wuSk5PJyHh4SQh7e3vKlCmDj4+PIdnf+ejr64ufnx+enp5P1KqkQiX2O000XnrpJX766SdCQ0MNz7Vp04YPPvjAtNEJYSaKohAbG8upU6c4deoUp0+fJjo6muTkZPV5jR1Z5eqTWbU5irYtk7LTGZj3G/1d/qSGNpHxtRN5L6QMCTVexL7+K/iWr4RWKw23H8TR0REvLy+8vAq2jj83N5fk5GQOHjyIm5sbSUlJJCYmGj7euHGDhIQEEhMTycvL4/r161y/fv2Bx9NqtYYk7+/vj5+fH76+vobPy5QpYzW/cAvCqDn2U6dOUa9evXz7AgMDuXTpkiliEsKk9Ho9V69e5fTp02zfvp3ExEROnz593zljBXCtHkZyhQgy7NX7MvxL2jOqZX06NuyOnS4N9k2HXVNwy7xBlRNRcPk39eJrg1fBSe7lMAUHBwd8fHyoXr36Q6eY8vLySEpKIj4+noSEBMPHhIQEQ7K/ceMGOp2OmJiYe1pw3uHo6Iifnx/ly5enfPnylCtXLt9mzp4O5mBUYq9WrRqzZ89mwIABhn3Tpk2jZs2aJgtMCGPpdDpOnjzJwYMHOXToEEePHjX8OX/36hFHR0dq1KhBzZo1qVmzJnbeVZh3IpN9l1MA8HTTMqJlDXqFVcTR/u+zOefS8Mw78PR/4NAvsGMS3IqFjR+od7KG/0ftyer6+D0KxKPZ29vj4+Pz0HLhOTk5JCQkcO3atXu2uLg44uPjycnJeWji9/T0pFy5clSoUIEKFSpQsWJFw8fieGOmUYn9vffeY/Dgwfzyyy/4+voSFxeHXq9nxowZpo5PiEfKzMzk2LFjhkR+/PhxdLr8beS0Wi0BAQGUKVOGpk2b8tRTT1G1alUcHBzI1OXy2aqT/LrmCgBODnYMaFqFwc2rUcr5AWu4ta5qHfj6/dU68H99B8nnYesX6rLJhq9C+FAo5Wfu4YtHcHR0NJx5349eryc+Pp7Y2FiuXr1KbGxsvs9TU1MNK5uOHTt2z9e7u7sbEv2dZF+5cmUqVKiAi4tlWhUaldhDQ0PZsGEDW7ZsISEhAV9fXyIjI01ykUWIR8nJyeHw4cPs2rWLAwcOcOrUqXw35oB6hhUSEkJoaCjBwcFUr14de3v7e1aPnIhLZdivh7hwQz2j7xJSjnfaBFLOvYD/IR20ENoHgl+Ck8vhz28h/pi6ombPj1C3JzR5C7xrmGz8wrTs7OwMF7wbNGhwz/Pp6enExsZy5cqVfFtMTIzhQm9KSgpHjx6952t9fHyoXLkylSpVyrf5+vqadUxGr2N3d3enc+fOJmuNJ8TDJCQksGPHDnbu3MmePXvu6f3o7+9PcHAwoaGhhIaGUqFChYeuglAUhdk7L/HFmtPo8vT4lnLmu57BNKpmZJEuO3sI6gK1O8O5jeq0zJXd6nTNoblQ8wVoOgLK35s4RPFWokQJAgMDCQwMvOe5zMxMrl69apjGuXLlCpcvX+by5cukpqYa5vv37t2b7+ucnJx45ZVXzNb2z6jEnpOTw/fff29ojbdy5UqGDx9ustZ4QuTm5nL06FF27tzJjh07OHfuXL7nPT09adKkCWFhYYSEhBTqDCgpPZtRi4+y+XQCAK1qleWrrnXxcDPBqhaNBgJaq1vMbnUO/swaOL1K3So1Vc/gH7PgmCgeXF1dCQgIICAg4J7nUlNTDUn+7u3KlStkZ2dz5coVs8VlVGL//vvvDa3xRowYgZeXl6E13qRJk0wdo3hCZGdns3PnTjZu3MjOnTvzrVrRaDTUqVOHJk2a0KRJEwICAoxannYoLoMJv/1JQlo2Wgc7xr3wFH3CK5lnjXPFcHVLOK3ezXp0IVz+S918aqsJPqgL2EstFltUunRp6tatS926dfPt1+v1htIS5lIsW+OJJ4dOp2PXrl1s3LiR7du355ticXd3p3HjxjRu3Jjw8PDHusU8J0/PxE1nmbLlKgpQ3acEk18M4Sm/Uo8/iEfxqQmdpkCL92H3FDgwGxJOwLJBsPkzCB+iztM7yTWqJ4GdnR1eXl7cvHnTbO9RbFrjiSeHTqdj9+7dbNy4kW3btuVL5r6+vrRq1YoWLVoQFBRkkn9TV5IzeXPBIQ79XbDrxbAKfNCu1gOLdZlN6XLQZjw0G6nWotkzDVKvwPqxsPVLaNBfXWlTyr9o4xI2x6h/2Xda440YMUJa44kC0ev17N27lzVr1rBt27Z8t4n7+PjQqlUrWrVqRe3atU06LbLm2DXeXXyUtOxcSjo7MLyxDwNa1330F5qTi4ea3BsNhSMLYNf3kBStlg/e9QPU6Q6N34CytS0bp7BaRq9j79evn7TGE4907do1Vq5cyYoVK/Ld8u3j40PLli1p2bKlyc7M76bL1fN/a04xe+clQG1NN6lXMFlJcSZ9n8fi6KKepYf2hbPr1CWSMTvhyHx1q/YsNB4GVZvLhVZRKEYl9ooVK7J69Wq2bNlCXFyctMYT+eh0OrZt28by5cvZs2ePYbquZMmSPPfcczz33HPUqVPHbFN3V5IzeePXQxy5kgLA4IhqvNM6AEd7O6KTzPKWj8fODmq2Vber+9UEf2oFnP9D3crWUc/ga3dR180L8QhGJfbPP/+ccePG0bZt23z7R48ezVdffWWSwIT1iY6OZvny5axZs4bU1FTD/oYNG9KxY0datGhhtmYQd/xxKp63Fx4h9XYOpV0c+bZHPZ59qqxZ39OkyjeAHj9D8kW1bd+hX9Qbnpa9Dhs/Uhtz1+8vJQvEQxU4scfHx7Nr1y4AFi1aRFBQUL7n09LS2Lhxo2mjE8VeTk4OGzdu5LfffuPEiROG/T4+PrRr146OHTs+8FZuk8aRp+frDWf4cdsFAOpVcOeHl0Io72FdxZsMPKtA26+g+Ri1q9PenyD9OvzxKWz/Guq9qK6m8TbPDS7CuhU4sXt4eDB37lySk5PR6XRERUXle97JyclQ1lfYvpSUFJYuXcrChQtJTEwE1IJMERERdOzYkUaNGhXZKqnrqVkM+/Ug+y6py8f6N6nM2OefMmnPUYtx9VQvtDYeBseXwu4f4Pox2D9D3QKeh0ZDQJGaNOIfBU7sWq2WxYsXAzBgwAAp+PWEunDhAgsWLGDVqlWGQlve3t706NGDTp06FXl5iT/P3WD4gsMkZego6eTAV93q8nwdG0xyDk4Q/CLU6wWX/lRXz5xdB2fXwtm1VPAIhOZvyzy8AIycY39QUs/NzcXBQdqo2hpFUdi9ezfz5883TMcB1KxZk5deeolWrVoVeSf7PL1C1B/niNp8DkWBWn6lmNI7lMrebkUaR5HTaKBKM3VLPKfOwx+ej9PNM3/Pw38IDV9T5+FLlLF0tMJCjMrCMTEx/PDDD8THxxuq6uXk5HDx4kV2795t0gCF5eTk5LB69Wrmz5/PhQvq3LVGo6F58+a89NJLBAcHW6Td2I20bIb/dogdfy9xeTGsIh+1r4Wzo32Rx2JR3jWg3bcQOY6kDV/jdX4ppF2DLePVefg63SF8MPjK/SVPGqMS+/vvv4+iKHh4eJCUlEStWrX4/fff6devn4nDE5ag0+lYt24d69atM6w9d3V1pWPHjvTq1atILoY+yJ4LSQz79RAJadm4ONrzf12C6BxS3mLxFAuuntwMehWvdh+rpYN3T4G4g3B4rrpVfkZtABLwnFqFUtg8oxL78ePH2bp1K3FxcUycOJFx48bRrFkzfvzxR7mAasV0Oh3Lly9n9uzZxMTE4OTkhLe3N3369KFjx44WvU9Br1eYtv08X68/g16BGj4lmNI7lBplpb6KgYMW6naHOt3g6j41wZ9coc7JX/oTPCpD2OsQ8jI4F0GNHGExRiV2FxcXSpcujYODA2fPngWgWbNmvPvuuyYNThQNnU7H77//zuzZs0lIUEvZenp6MmTIEDp16mT2teePcjNDx9sLD7PlzA1AbYbxeeegoq/1Yi00GqgQpm6pV2Hv/9TCYzcvqXVptoyH4N5qCz9ZLmmTjL7zdNu2bURERKDX67ly5QparZbc3FxTxyfMSKfTsWzZMmbPns2NG2rS9PHxoV+/fgQFBVGrVi0LRwiHYm7yxvxDxKbcxsnBjk861KZnw4c30RB3KV0eWn0CEaPVFn67p0HiGdj7o7pVbwVPD4ZqkeodsMImGJXYBw0axJtvvsmqVavo2bMnvXr1wt7ensjISFPHJ8wgJyeHZcuWMXPmTMMadB8fH/r370/Hjh3RarVER0dbNEZFUZi14xJfrD1FTp5CFW83fngplFr+MoVgFK0bNHhVXS1zfrN6w9PZ9RC9Ud28qqvTNMEvSvlgG2BUYo+MjGTDhg14eXkxZMgQKleuTHp6Op07dzZ1fMKE9Ho969evZ+rUqcTFqcWwypYty6uvvkr79u3RaovH+udbWTmMXnSUdSfUC7cv1PHjy651KPmgxtKi4DQaqP6suiWdh33T1dZ9SdGwdpR6Z2vI39M0XtUsHa0wklGJPSUlhSVLlhAbG5uvifChQ4f44osvTBacMA1FUdi1axfff/+94ZqIl5cXAwcOpGPHjkW+Bv1hjsemMnT+QS4nZeJor2HcC7V4pZGZOhw96byqwXNfQIv31PLBe36EpHNqnfg906BGazXBV3tWpmmsjFGJffjw4Vy7do3g4GBprlHMHT9+nKioKA4ePAiAm5sbffv25cUXX8TFxcXC0f1DURTm743hk5Un0eXqKefuwpTeodSr4G7p0GyfU0kIGwgNBsCFLWqCP7cezm1QN8+q0HAgBL8ELu6WjlYUgFGJ/ciRI2zZsuWxWpUJ87p06RJTpkxh8+bNgFoSokePHvTv35/SpUtbOLr8MrJzeW/ZMZYfVqeHWj7lw9fd6+HuWjymhp4Ydnb3maaZB8kX1NU0mz+Hej3VJF/W8hfWxYMZvSomJyfH1LEIE7hx4wY//vgjK1asQK/XY2dnR7t27Rg0aBC+vr6WDu8eZ66nMWTeAc7fyMDeTsO7zwUy8JmqMvViaYZpmvfh2ELY8xPcOKVWmtw/U73pKWwgBL4A9rLstLgx6ify4YcfMmjQIDp16nTP2V+nTp1MEZcopMzMTObMmcMvv/xCdnY2oN5b8MYbb1C1alULR3d/iw9cZdzvx8jK0eNbypnvXwqhQWWpM16sOJX4ZzXNpb/U1TSnV/9z01OpcurzoX2lNk0xYlRiX7x4MWfPnmXWrFn55tg1Go0k9iKWl5fH8uXLmTZtGsnJyQDUrVuXt956i3r16lk4uvu7rcvjoxXHWbj/KgDP1PBmYs9gvEpY9kYo8RAaDVR5Rt1Sr6pn7Qdmw61Y2PwZbPsv1O6sTtOUbyCt/CzMqMS+bt06li9fTvXqcteapSiKwo4dO5g0aRIXL14EoEKFCgwbNowWLVoU26mMCzfSGTLvIKevp2GngeEtAxjaojr2dsUzXnEfpcvDsx9Cs9FwYhns+x/EHlBvgDr6G/gFq9M0QV3Vvq6iyBmV2D08PKhYsaKpYxEFdPr0aSZOnMj+/fsBKF26NAMHDqRr167Fauniv606Gse7i4+SocvDu4SWqF4hNK7ubemwhLEcndUbmoJfVBP73ulwfAlcOwzLh8KGcRDSBxoOUOvUiCJj1FrFN998k7Fjx3Ly5EliY2OJi4szbMZITk6mVatW7Nmzx7DvyJEjdO/enZCQECIjI1m0aJFRx7Yl169f58MPP+Tll19m//79aLVaXnnlFX7//Xd69epVbJN6dm4eHy4/zhvzD5Ghy+PpKp6sefMZSeq2pFx96DwV3j4FLT+G0hXh9k3YGQWTgmF+Tzi3Ce6670WYj1Fn7GPGjAFg9erVhj/5FUVBo9Fw6tSpQh3rwIEDjBkzhpiYGMO+1NRUQ9mCnj17sm/fPoYOHUpgYCB169Y1JmSrlpmZyezZs5k7d66ha9Hzzz/PkCFD8PMr3t2CriRnMnT+QY5eVZtbD2lejbdbBeBgL/c/2CQ3L2g6Ahq/qa6B3/uTWsLg7Dp186iinsEH95aG3GZkVGL/448/TPLmy5YtIyoqilGjRjFixAjD/g0bNuDu7k7v3r0BaNSoEe3bt2fevHlPVGLX6/WsXLmSKVOmkJSkNpUIDQ1l+PDhxaJA16NsPBnPOwsPcysrF3dXR77rGUyLQB9LhyWKgp09BD6vbonRan/WQ/Pg5kV1imbz5xDUDcJeA/8QS0drcwqV2K9fv46vr6/JLsw1bdqU9u3b4+DgkC+xnzt3joCAgHyvrV69uqHn6pNg//79fPvtt4YSABUqVGD48OE0a9as2F4YvSMnT8+E9Wf4abvadSmkojvfvxRKOXe5kPZE8q6uromPHAfHFqlz8fHH/mkEUq6+upqmdmd13l48tkIl9rZt23Lw4EEiIyPvSS7GTMWUKXP/da8ZGRn33O7u7OxMZmZmYcIF1ObLrq6uhf66gsrIyDBpJcRr167x888/G643uLq60qNHD9q2bYujoyPnz5832Xs9jLHjupGRw+d/xHEiIQuArkEevNawDLcTY4lONHWUhWfqn1dxYTXjKt0UWjbBOfEopc8sokTMJjSxByD2AHlr3+VWtU6k1uhCbkm1K5bVjMsIxoytoDmwUIl99erVgOmmYh7ExcWFtLS0fPuysrJwcyt8o+KqVauatfNPdHS0SZZ9pqWlMX36dH777Tdyc3NxcXGhW7duDBo0yCKlG4wZ1/azNxi+4jDJGTpKOjkwoXs9ngsqXne7murnVdxY3bhq1IBGXSH9Bhz8GfbPwv7WVTxO/ozHyTlQoxU0GEC0SxXrGlchGPMzS09PL9DrCpXY71yomzVrFuPGjbvn+dGjR/PVV18V5pD3FRAQwI4dO/Lti46OpkaNGo997OImLy+PpUuXMm3aNFJT1QuMjRs3Zvjw4cX2jtF/y9MrTNp0lslbolEUqO1fiim9Q6nkVfhfxOIJU6IMNBsJTYarhcf2TVcvtv5dgKySmx+ED1KXTcqdrQVW4MQeHx/Prl27AFi0aBFBQUH5nk9LS2Pjxo0mCapVq1ZMmDCB2bNn07t3bw4cOGC4iGhL9uzZwzfffMOFC+pcdNWqVRkxYgSNGjWycGQFl5CWxVu/HmbXBfXibu+nK/JBu1o4O0rTZFEI9g5Q8wV1Szqv3tl6aC6OGdfgj09g6xdQqyM0fA0qPC13tj5CgRO7h4cHc+fOJTk5GZ1OR1RUVL7nnZycTNbI2sPDg5kzZzJ+/HiioqLw9PRk3LhxhIeHm+T4lhYTE8PEiRPZvn07oN5gNHjwYLp06YK9vfUkxF3nk3hzwSFupGXjqrXniy516BhcztJhCWvnVQ3ajIfIccRvnkbZmJXqDVDHFqlb2SC1Pk3dHtLt6QEKnNi1Wq1hVcqAAQOYMWOGSQM5c+ZMvsd16tRhwYIFJn0PS0tPT2fGjBn8+uuv5ObmYm9vT48ePRg4cCClSllPyze9XmHqtvN8s+EMegUCypZgSu/6VPcx37UM8QRydCGtWnvKthkBcYdg3ww4thjij8Pqt2HjR2pybzgAyta2dLTFilHr2GfMmEFeXp7h7HLbtm14enpSp04dkwZnK/R6PStWrGDKlCmGQl2NGzfm7bffpnLlypYNrpCSM3SM+O0w286qza+71S/PZx2DcNFaz18awgr5h0DH76H1Z3D4V3WqJumcuj5+/wyoEK6exdfqKEsmMTKxb968mXHjxrFz506mTJnCtGnT0Gg0vP/++/To0cPUMVq1gwcP8vXXXxvWo1eqVIl33nmHxo0bWziywjtwOZk35h/iWmoWzo52fNoxiB4NKlg6LPEkcfGARkMg/D9wcbua1E+vhiu71W3dGAh5GRr0Vzs/PaGMSuxTpkxh+PDh6PV65s6dy+TJk/Hy8mLEiBGS2P928eJFpkyZwpYtWwAoWbIkgwYNonv37jg4WFdjAkVRmP7nRf677jS5eoWqZdyY0juUmr7WM30kbIxGA1Uj1C3tOhyc808Z4Z1R6lbtWfUsPuC5J64ZiFGjvXLlCj169ODkyZPcvn2bJk2a4ODgQGJiMbgDxcLi4+P56aefWLlypaGDUZcuXRg8eLBVthJMzcxh5OIjbDwZD0D7ev580aUOJZyerP8oohgr6QsRo6Hp2+qSyf0zIfoPOP/3VtIf6veF0FeglL+loy0SRv3vdHFxISkpic2bN1O/fn0cHBw4ffo0Hh4epo7Paty6dYtZs2bx22+/GQp1NW/enCFDhljNevR/O3IlhaHzD3L15m209nZ82L4WvZ+uWOxLGogn1N1LJpMvwoFZcGgupMWpyyW3faXWrmnwKlRtofZ4tVFGJfauXbvSqVMnbt26RVRUFMePH+e1117j1VdfNXV8xV52djazZs3i559/NtwVFhoayhtvvGG1BcsUReH3Ezf5ce9ZcvIUKnq6MqV3KEHlilcTbCEeyLMKtPpU7dl6coV6Fh+zE06vUjePKuo8fPDLakVKG2NUYh82bBhhYWE4OTkRHBzMtWvX+PTTT2ndurWp4yu2cnNzWb58OZMmTTLUb6hRowZvvPEGjRs3ttqz2ltZOYxdcozVxxIAaFO7LF91q0dpl+JZ612Ih3Jwgrrd1S3h72bcRxaoVSY3fqhWmazVST2LrxhuMzc+FSqxHzhwgPr16wPw9NNPG/b7+fnh5+fHjBkzGDBggGkjLGYyMjJYs2YNv/76KzExMWRnZ1OlShX+85//0KZNm3w9YK3NibhUhs47yKWkTOw18P4LtejfpLLV/pISIh+fp6DtBLURyPEl6rr4a4fh2EJ1K/OUmuDr9QRn6/7rtFCJfeDAgRw8eNDwuGPHjixfvtzw+IcffrDZxH7mzBmWLFnC2rVruX37NqDeIduuXTuGDBlSbLsXFYSiKPy69wofrzyBLldPOXcXxjQrQ/vGVSwdmhCmp3VTL6SGvgKxB9Ulk8eWwI1TsHYUbPpI7dfa4FUoF2rpaI1SqMSuKEq+x/9uhffv561ddnY2GzZsYMmSJRw/ftywv0qVKnTt2pUOHToQFxdn1Uk9IzuX95YdY/lh9Wf5bE0fvulRj8S4mEd8pRA2oFyourUeD0cXqlM1N07BoV/Uza+emuCDuoGT9dxZXajE/u8/yR/12FpdvnyZJUuWsGrVKm7dugWAg4MDkZGRdOvWjZCQEJsY65nraQyZd4DzNzKwt9Mwqk0gg56pip2dBlm4Kp4oLu7w9CAIGwhX9qgJ/sTvcO0IrHwL1o9Tp2jq9wffoEcdzeJkMfJdcnNzee+999i8ebNhn7+/P126dKFDhw54etpOj8ZF+6/wwfLjZOXoKVvKie9fCqVhZdsZnxBG0WjUi6gVw6HNF3BkPuyfBcnn1ZLC+6ZD+TB1RU3tzuBYPLuCSWK/S2ZmJn/99Rd2dnY0bdqUbt26ER4ebtUXRP/tti6PD5cfZ9GBqwA8U8ObiT2D8SrhZOHIhChm3Lyg8TAIHwqXtqsJ/vQquLpX3daNheCX1LP4MgGPPl4RKlRiz83N5ffffzc8zsnJyfc4Ly/PVHFZRKlSpVi8eDGOjo4PbNtnzaIT0hk67yBn4tOw08DwlgEMbVEdezvrn1YSwmzs7KBqc3VLi1f7tB6YDSkxsHuKulVqqp7FP9VeXWJpYYVK7N7e3vnqsHt4eOR77OVl/Qv9/f1t85bj5YdjGbv0GJm6PLxLOBH1YjCNq3lbOiwhrEvJsvDMO2rHp/Ob1bP4s2vh8l/q5uoFwb2hfj+1rryFFCqx3z33LKxDVk4en646yfw96iqX8KqeRL0Ygk9JKW0qhNHs7NW+rDVaQWqsWoTs4By1fMGdImRVItSz+MAXwEFbpOHJHLsNu5SYwZB5Bzl57RYaDQxrUZ23WgbI1IsQplS6HLQYC81G/V2EbBZEb4KL29TNrYxaSji0r1rqoAhIYrdRa49dY/Tio6Rl5+LppuW7nsFEBNjedQMhio27i5DdvKyewR/6BdLj4a/v1K1apHqxNfB5s4Yiid3G6HL1/N+aU8zeeQmAhpU9iHoxBL/SxXNZlhA2yaMSPPsBNB8DZ9aqlSbPb/5nK+GLa8P3oHp1s7y9JHYbcvVmJkPnH+LIlRQAXo+oysjWgTja285yTSGsir0j1OqgbskX4eDPainh9Ou4XdkK9DXL20pitxFbzyQw/LfDpGTmUNrFkW971OPZp8paOiwhxB2eVdQCZM3fg7iDJKY7Y65SY5LYrVyeXmHiprN8vyUaRYG65Uvzw0uhVPB0tXRoQoj7cdBCxXCU6GjzvYXZjizMLjE9m7cWHGJHdBIAfcIrMa7dUzg52Fs4MiGEJUlit1L7LyUzdP5B4m9l4+Joz5dd69AxuJylwxJCFAOS2K2MoihM//MiX647TZ5eobpPCab2DqVG2ZKWDk0IUUxIYrcit7JyGLXoCOtPxAPQoZ4/X3Spg5uT/BiFEP+QjGAlTsbd4j/zDnA5KROtvR0ftK/Fy09XtIm68EII05LEbgUW7b/CuN+Pk/1327opvUOpV8Hd0mEJIYopSezFWFZOHh+vOMGCfVcAaB5Yhu96BOPhVrQFhYQQ1kUSezEVk5TJf+Yd4EScWsDr7b9rp9tJAS8hxCNIYi+G/jgVz4jfDnMrSy3gNalXMM/UkAJeQoiCkcRejOTpFb7deIYftpwHIKSiOz+8FIq/uxTwEkIUnCT2YiIxPZs3fz3EzvPqXaT9GlfmvbZPoXWQAl5CiMKRxF4M3H0XqavWni+71qVDPdts0SeEMD9J7BakKAozd1ziizWnyP37LtJpL4dS3UfuIhVCGE8Su4WkZeXw7pKjrDl2HYD29fz5Uu4iFUKYgGQRCzhzPY3/zD3AhcQMHO01jHuhFq80qiR3kQohTEISexH7/VAsY5ce43ZOHn6lnfmhdyihFT0sHZYQwoZIYi8i2bl5fLbqJHN3xwDwTA1vJvYMxquEk4UjE0LYGknsReDqzUyGzjvIkaupALz5bA3eerYG9nIXqRDCDCSxm9ndvUjdXR35rmcwLQJ9LB2WEMKGSWI3kzy9QtQf54jafM7Qi3RK71DKe0gvUiGEeUliN4PE9GyGLzjMX9GJALwcXpEP2tWSXqRCiCJRrBP7mjVrGDlyJE5O/1xgbNmyJRMmTLBgVA/3716k/9cliM4h5S0dlhDiCVKsE/uxY8fo2LEjX3zxhaVDeaR/9yKtVsaNaS/Xl16kQogiV+wT+/PPP2/pMB4p9XYOoxdLL1IhRPFQbDOPXq/nxIkTuLi4MH36dPLy8oiIiGDkyJGULl26wMe5cOECrq7mu2B57MpNvvrtAtfScnC00/Cf8DK0f6oE165cMtt7FoWMjAyio6MtHYbJybisi62OC4wbW2ZmZoFeV2wTe3JyMrVq1aJNmzZERUVx8+ZN3n33XUaNGsVPP/1U4ONUrVqVEiVKmDw+RVFYsO8KH266QU6eQjl3F6a+HErd8u4mfy9LiI6Opnr16pYOw+RkXNbFVscFxo0tPT29QK8rtond29ubefPmGR67uLgwatQoevToQXp6ulmSdUFdT83iq/WnWXowFoBna/rwTY96uLtKL1IhhOUV28R++vRpVq1axTvvvGMojqXT6bCzs0OrLfoEeluXx4aT11l84Co7ohPRK2Cngf4NvHm/cwPpRSqEKDaKbWJ3d3dn3rx5lC5dmv79+5OQkMCECRPo3LlzkSV2RVHYd+kmSw5cZfWxa6Rn5xqea1DJg5FtAvHW35SkLoQoVoptYvf19eXHH3/k22+/ZerUqTg5OfHCCy8watQos793TFImSw5eZemhq1xJvm3YX97DhS6h5ekaWo5KXm4AREffNHs8QghRGMU2sQOEhYWxYMGCIns/Xa6eN+YfZMPJeMM+N609bev40bV+ecIqe8rZuRCi2CvWib2o3dblsf3cDTQaaFrdm66h5WlduyyuWvk2CSGsh2Ssu5R2deSPd5rjYKehbClnS4cjhBBGkcT+L+XcXSwdghBCPBY7SwcghBDCtCSxCyGEjZHELoQQNkYSuxBC2BhJ7EIIYWMksQshhI2RxC6EEDbGZtexK4oCFLx+sbEyMzPN/h6WIOOyLjIu62PM2O68/k5+exCbTewZGRkAREREWDgSIYQwrYyMDEqWfHA/ZY3yqNRvpfR6PQkJCbi5uRnquQshhDVTFIWMjAx8fHyws3vwTLrNJnYhhHhSycVTIYSwMZLYhRDCxkhiF0IIGyOJXQghbIwkdiGEsDGS2IUQwsZIYhdCCBsjid0ISUlJDBkyhAYNGvD0008zfvx4cnNzLR3WY0lOTqZVq1bs2bPHsO/IkSN0796dkJAQIiMjWbRokQUjLJzTp0/Tv39/wsLCaNKkCaNHjyY5ORmw7nHt2rWL7t27ExoaSpMmTfjss8/IysoCrHtcd+Tl5dGnTx/GjBlj2Gft41qzZg21atUiJCTEsI0aNQow49gUUWgvv/yy8s477yiZmZlKTEyM8sILLyj/+9//LB2W0fbv36+0bNlSCQgIUHbv3q0oiqKkpKQoYWFhyty5c5WcnBxl586dSkhIiHLkyBELR/tot2/fVpo0aaJMmjRJyc7OVpKTk5WBAwcqr7/+ulWPKykpSalTp46yZMkSJS8vT4mPj1fatWunTJo0yarHdbeJEycqNWvWVN59911FUaz73+EdX375pTJmzJh79ptzbHLGXkiXL19m7969jBo1ChcXFypUqMCQIUOYN2+epUMzyrJlyxg5ciQjRozIt3/Dhg24u7vTu3dvHBwcaNSoEe3bt7eKccbFxVGzZk2GDh2KVqvFw8ODnj17sm/fPqsel6enJzt37qRLly5oNBpSUlLIzs7G09PTqsd1x65du9iwYQOtW7c27LOFcR07doygoKB79ptzbJLYC+ncuXO4u7tTtmxZw75q1aoRFxfHrVu3LBiZcZo2bcrGjRtp27Ztvv3nzp0jICAg377q1atz+vTpogzPKFWrVmX69OnY29sb9q1fv57atWtb9bgASpQoAajF7dq3b0+ZMmXo0qWL1Y8rKSmJ999/n2+++QYXFxfDfmsfl16v58SJE2zdupUWLVrQrFkzPvjgA1JTU806NknshZSRkZHvHx5geJyZmWmJkB5LmTJlcHC4t8jn/cbp7OxsdWNUFIXvvvuOLVu28P7779vMuDZs2MD27duxs7PjzTfftOpx6fV6Ro0aRf/+/alZs2a+56x5XKBeu6pVqxZt2rRhzZo1LFiwgEuXLjFq1Cizjk0SeyG5urpy+/btfPvuPHZzc7NESGbh4uJiuCh3R1ZWllWNMT09nTfffJOVK1cyd+5cAgMDbWJcoCaAsmXLMmrUKP7880+rHtePP/6IVqulT58+9zxnzeMC8Pb2Zt68eXTr1g0XFxf8/f0ZNWoU27dvR1EUs41NEnsh1ahRg5SUFBITEw37zp8/j6+v70PrI1ubgIAAzp07l29fdHQ0NWrUsFBEhRMTE0PXrl1JT09n8eLFBAYGAtY9roMHD/Lcc8+h0+kM+3Q6HY6OjlSvXt1qx7V8+XL27t1LgwYNaNCgAatWrWLVqlU0aNDAqn9eoK7O+vrrr/M1xtDpdNjZ2VG3bl3zje2xL78+gV588UVlxIgRSlpammFVTFRUlKXDemx3r4pJTk5WGjRooMyaNUvR6XTKrl27lJCQEGXXrl0WjvLRUlJSlObNmytjxoxR8vLy8j1nzeNKT09XIiIilP/7v/9TsrOzlatXryrdunVTPvroI6se17+9++67hlUx1j6ua9euKcHBwcpPP/2k5OTkKLGxsUqPHj2U9957z6xjk8RuhBs3bijDhg1TwsLClPDwcOXLL79UcnNzLR3WY7s7sSuKohw9elTp2bOnEhISojz77LPKkiVLLBhdwc2cOVMJCAhQ6tWrpwQHB+fbFMV6x6UoinLu3Dmlf//+SoMGDZQWLVoo3377rZKdna0oinWP6253J3ZFsf5x7dmzxxB/eHi48tlnnylZWVmKophvbNJoQwghbIzMsQshhI2RxC6EEDZGErsQQtgYSexCCGFjJLELIYSNkcQuhBA2RhK7EELYGEnsQghhYySxCyGEjZHELswuOzub69evWzqM+yrOsRVner2e5557jgkTJuTb/+effxIUFMTatWstFJkASezCSH369GHy5MkFeu1LL73Ezp07zRyRce6OLS4ujpCQEOLi4iwc1T+uXr1KYGAgV69eNelxH3esdnZ2vP766/z666+kpqYCaiXDt956ixEjRvD888+bMlxRSJLYhdndvHnT0iE80N2x+fv7c+jQIfz9/S0YUdEwxVjbt2+Ph4cHv/zyC9evX2fQoEF07NiRAQMGmDBSYQxJ7OKx3DmjXLRoEZGRkdSvX5/+/fsbpjdeffVV4uLi+Oijj/j0008BtVb64MGDefrpp2nRogXfffedocb4neN9+eWXNGzYkNdee4133nkn33sOHz6cTz75pMDHKmhs/z47PnPmDAMHDiQsLIxmzZrx8ccfk5aW9sjj/tvo0aMfOobNmzfTq1cvGjVqRL169Xj55Ze5dOnSA7/Xd5+9T5482dCg4mHfi4cdq7DjucPBwYGBAwfyyy+/MHDgQGrVqsW4ceMe+jWiaEhiFyaxdetWfv/9d9avX09iYiJTpkwBYObMmfj7+/PJJ5/w4YcfkpmZSb9+/ahRowbbt29n/vz57Ny5855pnYyMDHbs2MHgwYPZtGkT6enpANy6dYvNmzfTrVu3Ah+roLHd7ebNm7zyyitUr16d7du3s2TJEi5evMjo0aMfedx/69GjxwPHcP36dd566y0GDRrErl272Lp1K4qi8MMPPxTq+1/Q78XDFHQ8d2vfvr2hg9i3336br8+ssBxJ7MIkBg4cSKlSpfD29iYyMvK+Z5ygJg+dTsfbb7+Nk5MTfn5+vPXWW/d0Zu/UqRNarZYGDRrg5+dnuBi3atUqqlatSu3atQt8rILGdrc//vgDR0dHRo4cibOzM2XKlOGDDz5g8+bNhu5ZBT3uw8bg6enJ6tWriYyMJD09nevXr+Ph4UF8fPwjYzTm+/owxnyfPvvsM0D9RShJvfi4t4uxEEbw9vY2fO7g4MCDyvzHxsaSnJxMw4YNDfsURSEnJ4ekpCTDPh8fH8Pn3bt3Z/ny5XTv3p1ly5bRvXv3Qh2roLHdLSkpCX9//3zJqnz58kaN+WFjcHR0ZNWqVSxYsACNRkNAQADp6en3bTD+MI/6Xnh5eT3yGIX9Pk2cOJGtW7eycOFC+vXrx+LFi+ndu3eh4hbmIYldFClfX18qVqzIunXrDPvS09NJSkrC09OT2NhYADQajeH5zp07M3HiRHbu3MmZM2do165doY5ljHLlyhEXF0deXp4hucfExAA8cN76YR40hrVr1zJ37lx+/fVXKlWqBKhnwWfPnr3nGHfiyMnJMey7c/H3Ud8LU1u0aBGzZs3i559/pmbNmvTt25fp06fTo0cPHB0dTf5+onBkKkaYnVarJS0tDYAWLVqQkZHB9OnT0el03Lp1i3fffZcRI0bkS+Z38/T0pEWLFowbN47WrVtTunRpo4/1sNjuFhERAcDXX39NVlYWN27cYPz48YSHhxu1kuRBY0hLS8POzg5nZ2cURWH79u38/vvv+ZL3HV5eXpQuXZrVq1ejKAonTpwwJHJTfC8Katu2bXzyySdMmDCB4OBgQF3+mp6ezvLly036XsI4ktiF2XXr1o3vvvuOkSNHUqJECWbPns2ePXto1qwZLVu2xM7OjqlTpz70GD169CA2NpZu3boZ9hl7rAfFdreSJUsya9Yszp49S0REBO3ataNcuXJMmjSpcIN/xBg6d+5M48aNeeGFFwgPD2fq1Kn07duXixcv3vOXgVar5bPPPmPt2rWEhoby5Zdf0qNHD5N9Lwri+PHjDB8+nFGjRtG6dWvD/hIlSvDyyy/zv//9j7y8PJO+pyg86XkqhBA2Rs7YhRDCxkhiF0IIGyOJXQghbIwkdiGEsDGS2IUQwsZIYhdCCBsjiV0IIWyMJHYhhLAxktiFEMLGSGIXQggbI4ldCCFszP8D28lj7sHsFH8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 3))\n",
    "ax = fig.add_subplot(111)\n",
    "fig_ax = (fig, ax)\n",
    "\n",
    "X = [(1, 0)]\n",
    "Y = [(3, 0)]\n",
    "\n",
    "all_conds = [(i, -tau) for i in range(N) for tau in range(0, graph.shape[2]) if (i, -tau) not in X and  (i, -tau) not in Y]\n",
    "\n",
    "# Ground truth\n",
    "ax.plot(dox_vals, intervention_data[:, mask_dict[0][:,Y[0][0]]==False, Y[0][0]].mean(axis=1), \n",
    "    alpha=0.8,\n",
    "    color = 'black',\n",
    "    linestyle='solid',\n",
    "    label=\"Truth\")\n",
    "\n",
    "# Estimation without adjustment (nonlinear correlation)\n",
    "fit_predict_plot(X=X, Y=Y, graph=graph, dataframe=dataframe, \n",
    "                 estimator=MLPRegressor(random_state=2, max_iter=1000), \n",
    "                 dox_vals=dox_vals,\n",
    "                 data_transform=None, \n",
    "                 graph_type='stationary_admg', \n",
    "                 adjustment_set=[], \n",
    "                 confidence=False,\n",
    "                 boot_samples=50,\n",
    "                 seed=4,\n",
    "                 label='MLP-uni',\n",
    "                 fig_ax = fig_ax,\n",
    "                 verbosity=0)\n",
    "\n",
    "# Estimation with all other variables\n",
    "fit_predict_plot(X=X, Y=Y, graph=graph, dataframe=dataframe, \n",
    "                 estimator=MLPRegressor(random_state=2, max_iter=1000), \n",
    "                 dox_vals=dox_vals,\n",
    "                 data_transform=None, \n",
    "                 graph_type='stationary_admg', \n",
    "                 adjustment_set=all_conds, \n",
    "                 confidence=False,\n",
    "                 boot_samples=50,\n",
    "                 seed=4,\n",
    "                 label='MLP-all',\n",
    "                 fig_ax = fig_ax,\n",
    "                 verbosity=0)\n",
    "ax.legend(loc='best', fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The '-uni' approach suffers from confounding and the '-all' approach blocks the mediator GPP$_t$, both lead to biased estimates.\n",
    " \n",
    "Have a look at other tutorials on various methods and functionality of tigramite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tigenv",
   "language": "python",
   "name": "tigenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
